Table of Contents

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Purpose of the Document

The purpose of this deliverable is to provide an overview of the activities carried out so far in order to assess the overall progress of the project. Based on the observed progress and evolution, the planning for the next period might be subject to slight adjustments in order to ensure that the project maintains its high potential impact.

As stated in the DoW, this document will include, specifically:

• Project status

• Compliance with planning milestones

• Current and potential project issues, problems and risks

• Plans and actions for the next reporting period

Scope

This document contains a high-level overview of the activities carried out so far. It is not intended to provide a detailed description of how these activities have been accomplished. Such a description can be found in the corresponding deliverables.

Intended Audience

The intended audience includes every partner of the ONTIC project.

Suggested Previous Readings

The ONTIC DoW should provide enough background to understand the goals of the project and the activities through which these objectives are meant to be attained.

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Executive Summary

This deliverable provides a detailed description of the progress made so far by each partner. A comprehensive enumeration of both completed and ongoing tasks is presented.

In addition, this document presents the issues encountered by each partner as well as those that could surface during the second year.

Section 6 presents the project status at the task level. The situation of the work plan activities and the expected outcomes of each task described in the DoW is indicated.

Section 7 contains a detailed description of the activities that each partner has already completed, those that are currently being carried out and those that are planned for the next reporting period.

Section 8 examines the level of compliance of the project with the first milestone, set for M12. The results obtained so far on each work package are analysed in terms of their conformity with this milestone.

Section 9 describes issues found so far, as well as the solutions proposed. Additionally, this section presents potential issues identified by the partners of the project.

The contents of this document will serve as a basis to refine the planning for the subsequent periods.

Project Status

In this section, we show the ONTIC project status by the month M12 classified by workpackage and task. The actual status of each activity and expected outcomes is presented.

Activities in progress are accompanied by an estimated completion percentage as perceived by participating partners. Note that these figures are meant to be only illustrative.

6 The ONTIC consortium commits to issuing press releases within one month of the start of the project, as well as to issuing at least two press releases per year on all important milestones and events. The project should identify which specific partners will undertake this action. Such press releases will target the local or national press of the partner entrusted with this task. The press release will describe the goals of the project in simple, jargon free language and whenever possible highlight the benefits to the region/country and the importance of the local partner being part of a EU consortium. A PDF scan of the collection of published press releases will be included as annex to deliverables D6.4, D6.5 and D6.6.

IP (33%)

EC may set in motion Support Actions addressing e.g. best practices and standards work, technology transfer and cross-disciplinary training, research roadmaps and technology foresight, in areas especially relevant for the objective of this project. Therefore, ONTIC partners shall undertake -when invited -to contribute to and participate in focused concertation actions, IP (33%) themed seminars or special interest groups, for example the European Data Forum.

All presentations, contributions and publications (also online publications and web sites) even partially funded by the project shall include the project logo and prominently acknowledge the grant under which the project is funded, using the standard text in Article II.30.4 of the Grant Agreement.

IP (33%)

Details of all publications even partially funded by the project shall be uploaded to some specific and agreed Bibliographic social networks such as http://www.citeulike.org/, http://www.mendeley.com or http://www.bibsonomy.org/. Every such must be tagged with the project identification "ONTIC 619633".

Key Activities Declared by Each Partner

The ONTIC partners participate in all work packages to a certain extent. However, each partner develops key activities in specific work packages, tasks and deliverables. Firstly, each partner summarizes the activities already completed in which they have been involved during the first year. Secondly, the ONTIC partners identify and declare specific key activities that are still in progress or that they plan to be involved in during the second year. These activities should not be considered as definitive and their contents and adequacy to specific ONTIC tasks and work packages will be discussed in the 2 nd Plenary Meeting to be held in February 2015 in Turin.

Ericsson

Activities Completed 7.1.1

WP1/WP5

Level of adherence to the Agile methodology:

• The project team has worked on the use cases refinement, taking inputs from potential stakeholders. The initial use cases have been adapted to the Agile's User Stories format, as this is the way the requirements will be managed in the project.

• The project has provided a set of initial high level user stories (Epics) and a second level of more detailed user stories, being ready to be used as a guide for the implementation along the second and third years.

• Big Data Research and Development activities have been focused on the Big Data platform and dataset extraction task, development of a first Policy Governance Function mockup, etc. The outputs generated by the R&D activities along this first year of project will be used as a base to implement the provided User Stories.

• User Stories will be continuously refined along the following year of the project incorporating new inputs from the market. This principle is key in Agile methodologies. The changes will be incorporated on the User Stories as the whole project executes the so-called "sprints", and provides as an output to be checked with potential stakeholders.

• The Quality of Experience concept, including QoS and congestion control, has been developed in several workshops, speeches, conferences, etc. There is a general agreement about the importance of this concept in the Telecom and IT space.

WP6 Exploitation activities:

• A first business analysis of the proposed scenarios has been provided. It focuses on the justification of why the market is ready for receiving the proposed new solutions and the general strategy from the partners to reach it. The business analysis has been focused on the telecom and IT markets

• There have been internal dissemination activities and engagement activities with key players in the field.

• The QoE virtuous circle concept has been delivered. The need for automatizing the prediction and policies setup and actuation has been identified.

• A high level network control and optimization architecture has been proposed, so that it becomes the foundation for the ONTIC use cases.

• Some preliminary ONTIC outcomes have been protected as a patent application.

Implementation activities:

§ Ericsson will show at the end of the first year review a first mockup of the PGF functionalities, part of the overall architecture.

Standardization activities:

§ Identification of 3GPP working groups where the ONTIC results could be applied or where to align the ONTIC outputs: User Plane Congestion (UPCON). § UPCON follow-up. Attendance to 3GPP meetings. Plans and Actions for the Next Reporting Period 7.1.3

Activities in Progress

WP5

Level of adherence to the Agile methodology:

• The project will work in Sprint Cycles. Early in the second year the project will define the Sprint durations and will prioritize the backlog that later on will be implemented by the partners.

• The outputs generated from the Sprints will be used as tools to engage with potential receivers of said solutions.

• The project will work in the market side receiving inputs from the market and refining the proposed user stories

• The implementation of the scenarios will be done by prioritizing the backlog and splitting these detailed user stories into affordable tasks.

• At the end of this reporting period, the project will deliver a first version of the PoCs for provided use cases Business Analysis activities:

• The project will provide more details on the exploitation side by incorporating more information from the inputs received from the market.

• The project will work on the standardization side.

UPM

Activities Completed 7.2.1

WP1

UPM has contributed actively in the edition and reviewing of D1.1 deliverable.

WP2

This work package deals with designing, deploying, managing and provisioning with real ISP network flows a Big Data Network Traffic Summary dataset named ONTS (ONTIC Network Traffic Dataset). To this end, UPM has designed, implemented and deployed a fully operation version of the dataset capture and provisioning subsystem which is running at Interhost (SATEC) facilities since month M04. These activities have been carried out in tasks T2.2 and T2.3. Finally, UPM has contributed actively in the edition and reviewing of D2.2 and D2.4 deliverables

WP3

The main goal of this work package is to design high scalable offline machine learning algorithms to traffic characterization. In particular, UPM has focused their research work in the context of unsupervised clustering algorithms. Regarding this objective we have studied and identified the strengths and weaknesses of the most predominant paradigm for horizontal parallelizing offline machine learning and data mining algorithms, the so-called Map-Reduce approach. Current platforms implementing this paradigm (e.g. Hadoop and Spark) have been studied in depth regarding the iterative nature of the clustering algorithms. In addition, we have identified and studied the existing Map-Reduce versions of the most cited offline clustering algorithms. These activities have been carried out in task T3.1. UPM has contributed actively in the edition and reviewing of D3.1 deliverable.

WP4

Work Package 4 focuses on the design of scalable online traffic characterization algorithms. UPM have devoted their efforts to conducting a thorough review of the state of the art on online clustering, classification and forecasting algorithms. The identified mechanisms have been considered for their possible applications to the problem of congestion control. UPM have also identified the basic requirements for the design of online algorithms and has designed a high-level architecture for an online traffic pattern evolution subsystem. The results of this work have materialized as significant contributions to deliverable D4.1.

WP5

UPM have compiled a comprehensive state of the art of algorithms for congestion control in diverse network environments. The key issues concerning this problem have been identified, and the possible advantages of using big data analytics have been described. In addition, UPM have identified the key technologies and concepts in the field of quality of service management. This work constitutes the bulk of UPM's significant contributions to deliverable D5.1. UPM have also helped in the identification of relevant standardization bodies and their activities related to big data analytics and network technologies.

WP6

During the seventh month of the ONTIC project, the first BigDap workshop took place. UPM hosted the event, and its members served as organizers, reviewers and chairmen. UPM also contributed to the workshop with two papers, and produced a proceedings volume containing all the accepted papers. In order to provide a full description of the workshop, UPM edited deliverable D6.8.

In addition to the BigDap workshop, UPM have also carried out several dissemination activities. Specifically, UPM have set up the website and accounts on different social networks, issued different press releases and produced a factsheet. Additionally, UPM have contacted a dozen of industrial and academic entities who have shown a keen interest on the activities being carried out within the project. These contributions are thoroughly described in deliverable D6.4.

Activities in progress/Plans and Actions for the Next Reporting period 7.2.2

WP1

UPM is in charge of the project coordination. The main activities we have developed during this year in this work package are the overall project management and coordination and the follow up to ensure that the project achieves successfully its stated objectives on time, within budget and with the expected level of quality. These activities are included in tasks T1.1. and T1.2 and will be developed during the whole life of the project.

WP2

UPM has designed and implemented specific feature engineering mechanisms to adapt and prepare the contents of the ONTS dataset to the specific data formats that will be used in WP3 and WP4. Specifically, WP3 will process network flows instead of raw packets and so, we are in the process of customizing Tstat, an open source tool that aggregates network packets into flows. Some scalability problems have arisen when TeraByte sized data files are manipulated, and we are considering using other open source options to cope with this problem. These activities are included in tasks T2.2 and T2.3.

We have designed a full set of experiments to determine the actual performance and scalability of the state-of-the-art Map-Reduce frameworks in order to know which is the best option to run the machine learning and data mining algorithms we are going to adopt in the three ONTIC Use Cases. Preliminary experiments will be run on Hadoop and Spark platforms installed in a local cluster at UPM. Later, the AWB cluster provided by EMC and Google Cloud will be used to test real scalability. These experiments correspond to activities included in task T2.1.

WP3

After having identified the state of the art on offline unsupervised clustering algorithms and the existing map-reduced versions of them, UPM will focus in the design and implementation of new proposals for clustering algorithms that increase the current performance by a factor of 10.

WP4

After having identified the state of the art on online unsupervised clustering algorithms and the existing streaming versions of them, UPM will focus on the design and implementation of new proposals for online clustering algorithms that increase the current performance by a factor of 10. In addition, online forecasting algorithms will be designed and implemented to be applied in network congestion prediction scenarios.

WP5

UPM has been involved mainly in two use cases: Use case 2 (Proactive Congestion Control and Prediction) and Use Case 3 (Dynamic QoS Management). Although these use cases are more or less related they have enough complexity to be studied in a separate way. With regard to the use case #2, we are defining a simple network congestion model to apply predictive machine learning algorithms on it. In the next period we will focus on designing and implementing a forecast algorithm for congestion control scenarios. Regarding the use case #3 we have proposed an architectural model for dynamic QoS management using analytics that was based on a previous Ericsson proposal. In addition, we have also studied the problem of mapping QoE to QoS parameters. However, in the next period, we will focus nearly all of our efforts to use case #2.

WP6

The extensive research on the state of the art conducted by UPM provides an accurate understanding of the open issues in the different problems to be addressed by ONTIC. Research on these issues has great potential to materialize in scientific papers, patent applications, contacts with industry and academia, contributions to the Big Data PPP and the rest of dissemination and exploitation activities declared in WP6. UPM target numerous prestigious international conferences and scientific journals whose topics are highly relevant to the goals of ONTIC. A non-comprehensive list of these is presented in deliverable D6.4.

Polito

Activities in Progress 7.3.1

WP3

WP3 consists of three main design and development tasks (T3.1, T3.2, and T3.3), which started in M03 and will end in M33, and an experimental evaluation task (T3.4), which is not started yet. Each of the design and development tasks addresses a very different class of data mining techniques: • T3.1 addresses unsupervised clustering • T3.2 focuses on supervised classification, • T3.3 aims at extracting correlations by means of association-rule mining. Hence, the structure and contribution of each task is similar in structure, but different in content. All tasks contributed to D3.1 (Available algorithms identification) with different content depending on the specific class of algorithms.

Plans and Actions for the Next Reporting period 7.3.2

WP3

During the next reporting period, WP3 activities will shift from mainly focusing on algorithm identification and analysis to the design and development phase, according to the work plan.

Each of the ongoing tasks will contribute on a very different class of data mining techniques: • T3.1 addresses unsupervised clustering • T3.2 focuses on supervised classification, • T3.3 aims at extracting correlations by means of association-rule mining. Results will be collected into the Deliverable D3.2 (Scalable algorithms for offline network characterization) at M24.

CNRS

Activities in progress 7.4.1

WP4

Three tasks started in WP4. Task 4.1 aims at designing the architecture of the online classification systems for ISP traffic flows. This task is currently well advanced. The software architecture and all its modules have been defined as well as the kind of information that is required as input and the information that is produced. This software architecture just requires further formalization for the interfaces between modules, and will certainly be refined depending on the results of tasks 4.2 and 4.3. Task T4.2 is devoted to the traffic pattern evolution subsystem. A quite complete state of the art on the topic has been proposed. Based on this, the specific architecture for this subsystem has been issued, as well as the basics of the algorithms to be further designed and developed in the next reporting period. Task T4.3 is devoted to the network anomaly and intrusion detection subsystem. Similarly to T4.2, a state of the art on the topic has been issued. A detailed software architecture has been proposed as well as a preliminary algorithm. The evaluation of this algorithm has been started on a limited amount of traffic traces just to serve as a proof of concept.

WP5

Task 5.2 includes the design and development of the 3 use cases. Use case #1 relates to anomaly detection. This use case has been completely defined according to the selected methodology and formalism. The different scenarios for this use case have been clearly defined.

Plans and Actions for the Next Reporting Period 7.4.2

WP4

During the next reporting period, WP4 will mainly focus on task 4.2 devoted to the traffic pattern evolution, and task 4.3 on network anomaly detection. For these two tasks, algorithms will be developed, and test performed on the traffic data collected thanks to the capture system defined and developed in the framework of WP2. Task 4.2 will mainly focus on the design of traffic pattern evolution techniques and the enhancement of existing ones.

In task 4.3, our research will focus on 3 aspects:

-How to improve the scalability of the detection and classification algorithm? -How to provide an abnormality score for the identified classes of traffic for an autonomous decision from the system of what is legitimate from what is not.

-How to extend the current system toward a distributed system for improving classification and detection accuracy?

-How to integrate root cause analysis mechanisms for improving the automatic decision making of the anomaly detection system? Task T4.4 on subsystem integration and testing will be launched. During the next reporting period, it will mainly focus on the testing of the system on large amount of traffic data.

WP5

The work on use case #1 will continue in the framework of T5.2. It will consist in transferring the results from WP4 to WP5 in terms of development and integration.

7.5 SATEC Activities Completed 7.5.1

WP6

All the tasks of this WP6 are active during the entire life of the project (from month 1 to month 36), but during the first year of the project we worked on getting all partial results are reflected in the following deliverables: Plans and Actions for the Next Reporting Period 7.5.3

WP4

During the next period SATEC plans to work in the transfer the results of WP4 to develop software modules which are going to implement: network anomalies detection algorithms and intrusion detection algorithms.

WP5

During the next period SATEC plans to work in the design of a computing architecture (online and distributed) to be used in the implementation of use case #1.

WP6

During the next period SATEC plans to continue working in activities related with dissemination and exploitation specially trying to carry ONTIC results to other domains such as: IoT (Internet of Things), Smart Cities, M2M (Machine to Machine), e-Health, etc.

EMC Spain

Activities Completed 7.6.1 EMC Spain has carried out the following activities during the first year:

• Collaboration with the rest of the partners gathering requirements for the ONTIC Big Data architecture (Deliverable D2.1: Requirements Strategy), and helping to define initial Big Data Architecture and provisioning sub-system: (Deliverables D2.2:ONTIC Big Data Architecture and D2.4: Provisioning Subsystem)

• Working closely to UPM partner in the installation/configuration of a laboratory with HADDOP cluster, SPARK and Pivotal HAWQ, which will be used for testing and doing a comparative study between these different frameworks. • As a member of the Program Committee for the first international workshop on Big Data celebrated in Madrid on September 2014, helping in reviewing and evaluating one of the papers sent to the committee.

• Created awareness and visibility of the ONTIC project inside EMC through internal channels.

Activities in Progress 7.6.2 EMC Spain will be involved in the following activities that have not been completed yet in the next year:

§ Keep working closely with the rest of the partners in the different WPs, giving support on all of them specially where knowledge and expertise of the EMC team is more valuable. § Completing tests and benchmarking of Big Data frameworks in UPM laboratory. § ONTIC project awareness and visibility, as this is an ongoing process that will last until the end of the project Plans and Actions for the Next Reporting Period 7.6.3

• EMC activities during the next period will be focus, but not only, in WP2, developing and testing the Big Data Architecture defined during the first year.

• Related with WPs 3 and 4, EMC will provide support in evaluating and testing algorithms when available over the UPM laboratory.

• For WP5, EMC will increase his support to the rest of the partners involved in this WP, defining and moving forward in use cases design and development.

• And finally, for WP6, EMC will keep working on give visibility, internally and in our partners/customer, about ONTIC project progress and results.

Compliance with Planning Milestones

This section discusses the level of compliance of each work package with the first milestone (MS1).

WP2

WP2 has progressed according to plan. The overall design of the Big Data Architecture can be verified using D2.2. The Provisioning Subsystem can be verified using D2.4. Deliverables D2.1, D2.2 and D2.4 have been produced according to schedule.

WP3

Deliverable D3.1 establishes the fundamental requirements and the key issues for the design of an offline network traffic characterization system. D3.1 has been produced according to schedule.

WP4

WP4 is perfectly compliant with the planned roadmap, and reached the first milestone MS1. The first deliverable D4.1 has been produced according to schedule.

WP5

D5.1 provides a general overview of the ONTIC scenarios and related user stories, which allow for the verification of an initial version of the Use Cases, as stated in MS1. User stories will be used throughout the project to collect requirements. This deliverable provides an introduction to the proposed architecture, as well as a summary of the related SoTA and the first version of the working backlogs.

D5.7 provides an overall vision about the different standardization bodies that could be targets for the ONTIC outcomes. Goals of the first year have been reached and the second year needs of more effort to reach the proposed goals.

9. Current and Potential Issues/Problems/Risks 9.1 WP2

The deployment, operation and maintenance of the Provisioning Subsystem has presented several issues. The trace capturing process has been hampered by hardware compatibility and driver issues of the network traffic card. We are planning to use the native API of the card to build a homemade driver instead of the standard libpcap library, which we have used so far.

We have also experienced several scalability problems with open source tool Tstat, which is being used to group network packets into traffic flows. We are searching for open-source alternatives that do not face the same problems when handling big data files.

WP3

So far, the activities of WP3 have focused mainly on state-of-the-art algorithms identification, and no problem has arisen. Therefore, WP3 did not present issues or problems during the reporting period.

WP4

Dealing with WP4, everything is in line with the planned work. No unplanned difficulties appeared during the first year of the project, and we are very confident given the quality of our first results on the results we will get until the end of the project.

WP5

The ambition level defined in deliverable D5.7 is really challenging. We are proposing to refine and reduce the scope of the associated task and allocate more resources to it.

Due to the different profiles of the partners involved in the project (coming from the academic, industrial and service worlds) a proper synchronization of all the business and technological activities included in deliverable D5.2 is required to reach the success. Agile processes and tools will help to achieve these targets.

Regarding the use cases, ONTIC is facing a challenging business and technological scenario. This situation is forcing partners to redefine user stories and the targets defined for them. This is something that should be managed properly to assure the success in the project outcomes.

WP6

The activities of task T6.1 have not presented any issues during the reporting period and some of specific outcomes (for example networking with stakeholders) are progressing faster than expected. The activities of tasks T6.2 and T6.3 have not presented any issues during the reporting period.

•

  Deliverable D6.1 (Public Project Presentation according to the EU specifications) kept open its three tasks during the first year of the project and the three will continue to progress until the end of the project: • Task T6.1 Dissemination, Training and Public Awareness [from month1 to month 36]: which will continue with the dissemination planned activities. This task will contribute in the next period to the development of the deliverables D6.5 (Progress on Exploitation and Dissemination Plans -Part I) and D6.9 (Big Data Analytics Workshop #2) • Task T6.2 Exploitation and IPR management [from month1 to month 36]: which will continue with the exploitation and IPR activities as described in the deliverable D6.3 (Exploitation Strategy). This task will contribute in the next period to the development of the deliverable D6.5 (Progress on Exploitation and Dissemination Plans -Part 1) • Task T6.3 Impact Management [from month1 to month 36]: which will continue with the activities focused in the positioning of ONTIC in different application domains. This task will contribute in the next period to the development of the deliverable D6.5 (Progress on Exploitation and Dissemination Plans -Part 1)

•

  Specification, design and implementation of parallel-computing optimized offline techniques for traffic classification and calculation of related usage metrics. • Evaluation of the developed algorithms in our cloud-based Big Data platform and in available commercial environments in collaboration with other partners. 7.7.3.3 WP4 • Specification, design and implementation of online techniques for traffic classification and calculation of time-evolving related usage metrics, involving multiple cluster instances and aggregation points. • Evaluation of the developed algorithms through simulations and in large-scale test environments in collaboration with other partners. 7.7.3.4 WP5 • Integration of the developed offline and online traffic classification mechanisms in a comprehensive system suite. • Design, implementation and testing of prototypes on the specified Big Data architecture for the defined use cases. • Support experimentation in the target test environments. 7.7.3.5 WP6 • Meetings with customers and industrial partners for promoting solutions utilizing Ontic expertise; the arrangement of two such meetings is already in progress. • Writing of technical papers. • Continuous exploitation of Ontic results into commercial activities. 619633 ONTIC. Deliverable 1.1 35 / 36 ! !

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverables

			!	!
	6.1.2	Task T1.2 Content and Quality Management Expected Outcomes
				Status
	Work Plan (Completed/In Progress/Not Started) Activity Description Description ONTIC Big Data Architecture requirements C	Target Deliverable Status (Completed/In Progress/Not Started) D2.2 D2.3, D2.6 (pending)
	Draft the project quality plan .1 Work Package WP1 Management Task T1.1 Administrative Project Management 6.1.1 Supervise the development and exchange of working documents as well as control the quality of the material. Task T2.2 Design of Traffic Summary Dataset Provisioning Subsystem C IP (33%) 6.2.2
	Work Plan Activity Description Supervise the dissemination of the results in conferences, journals and workshops. Re-evaluate the status and schedule of the project periodically. Work Plan Risks identified will be reported to the coordinator for management and mitigation. Quality checks will be performed upon approval of deliverables. Activity Description	Status (Completed/In Progress/Not Started) IP (33%) IP (33%) Status IP (33%) (Completed/In Progress/Not IP (39%) Started)
	Cover administrative aspects of the project management. Expected Outcomes	IP (33%)
				Status
	Assembly.	Description	Started) (Completed/In Progress/Not	IP (33%) Target Deliverable
	One Kick-off meeting at the beginning of the project. Work Efficiency	IP	D1.3	C
	General Assembly Meetings -every six months. Visibility of the Project	IP	D1.3	IP (33%)
	Project Board Meetings -every month. Scientific Value of the Project		IP	D1.3	IP (33%)
	Reporting documents		IP	D1.3
	6.2 Work Package WP2 Big Data Network Traffic Summary Dataset
	6.2.1	Task T2.1 Requirements and Functionalities of a Big data Architecture for
		Network Traffic Summary dataset
	Ensured efficiency and optimized outcomes from the involved partners Efficient project administrative and financial monitoring Work Plan IP IP	D1.1, D1.2 D1.1, D1.2 Status
	Documents connected with the project from the consortium to the EC, including Activity Description reporting documents, cost statements etc.	IP	D1.1, D1.2 (Completed/In Progress/Not Started)
	Definition of Big Data solution capabilities for ONTIC analytics and ONTS dataset.	C
	Mapping between supported ONTIC analytics and ONTS dataset to data requirements.	C
	Design of ONTIC Big Data Architecture.		C
				12 / 36

Preparation of a Consortium Agreement: The Consortium Agreement will be drawn up and signed by partners to address intellectual property, exploitation rights, regulations and safety before the project start. C Administrate the budget in order to match the plans approved by the General Survey of capabilities of different measurement technologies including NetFlow, port replication and optical splitting among others. C Topology, routing, and traffic analysis of network to identify candidate locations for measurement instrumentation. C Techno-economic study for each candidate location to decide the appropriate measurement technology. C Design and implementation of a software component to generate summary records by extracting statistical information from network packets. C Design and implementation of a software component to move and store summary records from sampling points to the Big Data platform. C Integration and final testing of Provisioning Subsystem. IP (90%)

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverable

	6.2.3	Task T2.3 Deployment, Configuration and Provisioning of Big Data Network
		Traffic Summary Dataset
		Work Plan
	Activity Description Expected Outcomes Status Description (Completed/In Progress/Not Deployment and configuration of local instances of a Big Data platform based Target Deliverable Status (Completed/In Progress/Not Started) IP (80%) Started) on T2.1 Big Data Architecture. Deployment and configuration of local instances of a Provisioning Subsystem Definition of the limitations of existing C algorithms and identification of novel C D3.1 based on T2.2. Test and validation of joint operations of Provisioning Subsystem and Big Data platform local instances. techniques NS Provisioning of ONTS dataset by deploying a Big Data platform and a Provisioning Subsystem in SATEC core network. Massively scalable clustering algorithms for NS D3.2 offline network traffic characterizations IP (33%) Experimental evaluation of the algorithms NS D3.3
	Progressive migration of ONTS dataset records from SATEC Big Data platform to EMC AWS Big Data cluster. Task T3.2 Scalable algorithms for supervised classification 6.3.2	NS
	Expected Outcomes Work Plan Status (Completed/In Progress/Not Activity Description Description Started) Big Data platform and Provision Subsystem deployed, integrated and fully operative in SATEC core network. Available classification algorithms identification and analysis. IP Definition of improvements and novel classification techniques.	Status Target Deliverable (Completed/In Progress/Not Started) D2.1, D2.2, D2.4. IP (90%) D2.3, D2.5, D2.6 (pending) NS
	ONTS dataset totally provisioned with Design and development supervised classification algorithms.	NS
	trillions of network traffic summary records SATEC Big Data platform and later migrated These records will be initially stored in obtained from the core network of SATEC. Classification algorithm assessment and experimental evaluation. IP	(pending) D2.3, D2.5, D2.6 and D2.7 D2.1, D2.2, D2.4. NS
	to EMC AWS Big Data Cluster.
	Design of the Architecture of a Provisioning System for Traffic Summary dataset. 6.3 Work Package WP3 Scalable Offline Network Traffic C D2.5, D2.6 (pending) D2.4 Implementation and Testing of Provisioning System for Traffic Summary dataset. D2.5, D2.6 (pending) IP D2.4 Characterization System
	6.3.1	Task T3.1 Scalable algorithms for unsupervised learning
		Work Plan
			Status
		Activity Description	(Completed/In Progress/Not
			Started)

Definition of improvements and novel unsupervised learning techniques. NS Design and development of unsupervised learning algorithms. NS

Unsupervised learning algorithms assessment and experimental evaluation. NS

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverable

	6.3.3	Task T3.3 Scalable algorithms for correlation analysis
	Definition of the limitations of existing
	algorithms and identification of novel	C	D3.1
	techniques
	Massively scalable classification algorithms for offline network traffic characterizations	NS	D3.2
	Experimental evaluation of the algorithms	NS	D3.3
	Define standard assessment metrics to evaluate algorithm performance.	NS	D3.3

Work Plan Activity Description Status (Completed/In Progress/Not Started)

	Available correlation-discovery algorithms identification and analysis.	IP (90%)
	Definition of improvements and novel correlation analysis techniques.	NS
	Design and development of correlation analysis algorithms.	NS
	Correlation analysis algorithms assessment and experimental evaluation.	NS

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverable

	Definition of the limitations of existing
	algorithms and identification of novel	C	D3.1
	techniques
	Massively scalable algorithms for offline network traffic correlation analysis	NS	D3.2
	Experimental evaluation of the algorithms	NS	D3.3
	6.3.4	Task T3.4 Scalability evaluation on enterprise-grade cloud environment

Work Plan Activity Description Status (Completed/In Progress/Not Started)

	Scalability evaluation definition in terms of performance metrics.	NS
	Selection of the cloud provider.	NS
	Dataset loading to the cloud storage of the selected provider.	NS
	Distributed analysis execution.	NS
	Experimental evaluation.	NS

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverable

	Definition of the performance metrics to evaluate the scalability of the algorithms	NS	D3.2, D3.3 (pending)
	Experimental scalability evaluation results	NS	D3.3 (pending)
	6.4 Work Package WP4 Scalable Online Network Traffic
		Characterization System
	6.4.1	Task T4.1 Architecture Design of online classification systems for ISP
		traffic flows

Work Plan Activity Description Status (Completed/In Progress/Not Started)

	Analyse data structure as provided by the network traffic dataset for designing the intelligent scheduler.	IP (60%)
	Analyse the parallelization capabilities of algorithms designed in tasks 3.1,
	3.2, 3.3, 4.2 and 4.3, and design the related parallelization/synchronization	NS
	mechanisms.
	Definition of the assessment metrics of the system performance.	NS

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverable

	6.4.2	Task T4.2 Traffic Pattern evolution Subsystem
	The architecture for running in an integrated
	and parallel way all the functions for the online classification system with real-time	C	D4.1
	constraints (in D4.1).
	Experimental parameters for evaluating the entire online classification system (in D4.2)	NS	D4.2

Work Plan Activity Description Status (Completed/In Progress/Not Started)

	Set up the system connected to the flowing data.	IP (50%)
	Define the algorithmic processing that has to be applied on the data.	IP (30%)
	Check if the system is able to identify the patterns (clusters).	NS
	Tune the system if required.	NS
	Add additional machines to the cluster and check the scalability.	NS

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverable

	Scalable architecture able to process the traffic data as it is flowing (in D4.1)	C	D4.1
	Visualization tool	NS	D4.3 (pending)
	Parameter optional (Number of clusters,
	radius, min density threshold, window time,	NS	D4.2 (pending)
	etc.
	6.4.3	Task T4.3 Network Intrusion Detection Subsystem

Work Plan Activity Description Status (Completed/In Progress/Not Started)

	Available systems identification and analysis.	IP (90%)
	Design of a robust clustering algorithm for anomalies and intrusions detection coping with the curse of high dimensionality.	IP (33%)
	Design of an autonomous method for anomalies abnormality ranking.	IP (33%)
	Design and development.	IP (33%)

Expected Outcomes Description Status (Completed/In Progress/Not Started) Target Deliverable

	!	!
	Expected Outcomes Status Detailed report for summarizing ONTIC standardization effort. IP	D5.7 D5.8, D5.9 (pending)
	Characterization Systems Description Document describing ONTIC regulatory aspects with recommendations to relevant regulatory institutions.	(Completed/In Progress/Not Started) IP	Target Deliverable D5.7 D5.8, D5.9 (pending)
	Definition of the limitations of existing anomalies and intrusion detection systems and issuing novel techniques. Massively scalable, efficient, and dimensionality insensitive clustering algorithm for anomalies and intrusion detection. Integration of key project results in the test-Work Plan IP IP bed environment. NS 6.6 Work Package WP6 Exploitation, Dissemination and D5.2, D5.3, D5.4, D5.5, D4.2 D4.2 D5.1 Status (Completed/In D5.6, D5.10 (pending) A set of scenarios for the validation of key D5.1 Standardization Activity Description Progress/Not ONTIC concepts. IP D5.2, D5.4, D5.5, D5.6 Started) (pending) Task T6.1 Dissemination, Training and Public Awareness 6.6.1
	Autonomous ranking for anomalies Integrate ONTIC online and offline mechanisms. Evaluation of the concepts and identification	D5.1	NS
	abnormality, and automatic defense filtering rules generation for security device online Parameterize and characterize mechanisms, techniques and algorithms to be IP used by data mining developers in other application domains. of topics for further research. IP D5.2, D5.3, D5.4, D5.5, D5.6 D4.2 NS (pending) Work Plan
	configuration. Experimental evaluation of the subsystem on scalability, robustness, accuracy and Set up the ONTIC framework for developing autonomous, proactive and NS scalable characterization systems. Task T5.3 Standardization and Regulatory Activities 6.5.3 Activity Description temporal efficiency. T4.4 Subsystems Integration and Testing 6.4.4 Work Plan Activity Description Expected Outcomes Description Work Plan Status (Completed/In Progress/Not Target Deliverable D4.3 Status NS (Completed/In Progress/Not Status (Completed/In Progress/Not Started) Started) Status (Completed/In Activity Description Progress/Not C Started) An integrated online and offline network NS Started) Create the project website, under the .eu domain. C D5.3 (pending) traffic characterization system An open source framework for developing highly scalable autonomous characterization NS Prepare and maintain a dissemination plan including conferences, publication IP (90%) possibilities and clustering activities. D5.10 (pending) Disseminate the adoption of ONTIC outcomes to other application domains systems. (e.g. bioinformatics, genomic, medicine, physics, social sciences, finances, IP (33%)
	marketing).
	Task T5.2 Verification and Field Trials Install publication repository including tutorial and training material. 6.5.2 Coordinate and support the joined publication of project partners and external user group participants.	NS NS
	Expected Outcomes (Completed/In Progress/Not Define scenarios and roadmap of global verification and field trials. Description Build and disseminate recommendations for future regulation activities. Target Deliverable IP (33%) Started) NS Status Work Plan Progress/Not Evaluation of regulatory impacts of the project architecture and solution. IP (10%) Activity Description Status (Completed/In IP (90%) the system. IP (33%) Identification and analysis of legal problems linked to the implementation of Presentation of project results at booths at conferences including proofs of concept of the context aware service platforms developed in the project. IP (33%)
	Started) NS NS Select final scenarios for implementation in experimental driven approach. An integrated system for real evaluation Demos on some specific use cases Expected Outcomes Use cases definition. Prototypes design, implementation and set-up. Global verification and fields trials to demonstrate the operation of online and offline ONTIC mechanisms and techniques. Status Evaluate the concepts and identify the most significant problems to indicate further research. Started) Description (Completed/In Progress/Not Target Deliverable D4.3 D4.3 NS IP (20%) IP (50%)
	Document describing ONTIC standardization		D5.7
	strategy.		IP	D5.3, D5.8, D5.9, D5.10
				(pending)
		20 / 36

Integrate the algorithms developed in tasks 3.1, 3.2, 3.3, 4.2 and 4.3, and the architecture issued in task 4.1. NS Develop the visualization subsystem. NS Test the full system and evaluate its global performance level. NS Issue some demo on some specific use cases. NS 6.5 Work Package WP5 Implementation, Integration and Verification Task T5.1 Integration of Offline and Online Network Traffic 6.5.1 Definition of ONTIC standardization strategy. Analysis of existing standardization Work Items that is relevant to the context of the project solution design and aim of the project. IP (90%) Identify which standardization bodies are most relevant to ONTIC objectives. Define the field of contribution for each standard. IP (90%) Actively participate in the standardization work in the relevant standardization bodies IP (33%) Using the outcomes of the research and experimentation of ONTIC for shaping standardization efforts. NS Analysis of the International and European regulatory framework.

Create the project branding (templates, colours and fonts to be used), produce a project logo of professional quality and use it consistently on its web site and its communications.

C

Prepare the initial set of communication materials (flyers, posters, pop-up stand and banner).

Work Plan Activity Description Status (Completed/In Progress/Not Started)

	6.6.2	Task T6.2 Exploitation and IPR management Expected Outcomes
					Status
	Work Plan (Completed/In Progress/Not Started) Activity Description Description Fully and complete answer to stakeholders needs. IP Awareness-raising and sensitizing activities IP identification, protection and mediation High market and research impact of the project results. IP	Target Deliverable Status (Completed/In Progress/Not Started) D6.4 D6.5, D6.6 (pending) IP (33%) IP (33%) IP (33%) D6.4 D6.5, D6.6 (pending)
	Whenever such Bibliographic social networks allow for a catalogue of publications to be retrieved by tag or published as an RSS feed, the project's All presentation materials for which this is appropriate shall be published on Business development web site should expose such a Market and application studies Exploitation roadmap Integration of current market trends and need into project framework IP D6.5, D6.6 (pending) IP (33%) IP (33%) IP (33%) IP (33%) D6.4
	the project's web site under a Creative Commons licence	IP (33%)
	(http://creativecommons.org/) or another appropriate license. All open source software produced by the consortium shall be published on Expected Outcomes
	publicly available	software	repositories	such as Status	NS
	http://sourceforge.net/, http://github.com/ or http://osor.eu. All data sets for which this is appropriate shall be published on the project's web site under a Creative Commons licence (http://creativecommons.org/) Description (Completed/In Target Deliverable Progress/Not NS Started) or another appropriate license. Clear IP situation for each technology D6.4 IP D6.3, D6.5, D6.6 (pending) Expected Outcomes Status Exploitation roadmaps and clear paths to market entry for the major technologies D6.4 IP D6.3, D6.5, D6.6 (pending)
		Description		(Completed/In Progress/Not	Target Deliverable
	6.6.3	Task T6.3 Impact Management	Started)
	Public project presentation		C	D6.1
	Project Website			C	D6.2
	Dissemination plan including conferences and journals.	IP	D6.4 D6.5, D6.6 (pending)
	Publication repository for conference papers, presentations, journal papers, standard contributions and patents. Networking with stakeholders Establishment of the Advisory Board (AB). Management of the AB.	IP IP	D6.8 D6.9, D6.10 (pending) C D6.3, D6.4 D6.5, D6.6 (pending) IP (33%)
	Project Showcase Identify additional stakeholders and players like manufacturers, application NS D6.7 (pending) developers and research institutions. IP (33%)
	Prepare information and support material for the information of the potential stakeholders in the domains of Big Data and Network management.	IP (33%)
	Monitor and evaluate the impact of the project's activity and give feedback
	to the relevant work packages and tasks, mainly task 1.2 on quality	NS
	management.

§ Task 5.2 Verification, Use Cases and Field Trials [M1--M36]:

  Ericsson will lead the continuous development and prioritization of the user stories and the related backlogs. Ericsson will also coordinate the landing of the already defined use cases into the first version of the different prototypes § Task

	7.1.2
	7.1.2.1 WP5

5.2 Verification, Use Cases and Field Trials [M1--M36]:

  Development of thePolicy Governance Function to be integrated in the Prototypes. Close coordination with the analytics and the policy enforcement nodes. §

Task 5.3 Standardization and Regulatory Activities [M01--M36]: 3GPP

UPCON follow-up 7.1.2.2 WP6 § Task 6.2 Exploitation and IPR management [M01--M36]: Continuous engagement with the market in order to enrich the user stories and to get new requirements, synchronizing activities between research and development activities and the business activities. § Task 6.2. Supporting other partners to identify ONTIC outcomes that could be protected by means of patent application.

•

  One of the reviewers for deliverables D3.1: Available algorithms identification,

	D5.1: Use Case requirements, D6.2: Project website and D6.8: Big Data analytics
	workshop #1
	• Contributions to deliverables D6.3: Exploitation Strategy and D6.4: Exploitation and
	Dissemination Plans

  Updated company's Web-site with Ontic information; issue of an announcement about the participation of the company in Ontic to be distributed by targeted e-mail campaigns and through publications of Ethos Media, reaching Greece and South East Europe. • Co-authoring of a technical paper, "A Telecom Analytics Framework for Dynamic Quality of Service Management" appeared in Big Dap'14 conference. Investigation of and experimentation with configuration options for Hadoop (number of worker nodes, CPU, RAM per node. • Integration and testing of processing components for creating traffic summary records. Preparation of a meeting with First Data Hellas for a pilot project on Big Data analytics for cloud infrastructure monitoring. • Promotion of Ontic activities in our stand at the Mobile World Congress in Barcelona in February 2015. Test of joint operations of the provisioning system and our Big Data platform. • Support of the migration of dataset records from the SATEC Big Data platform to EMC AWS Big Data cluster.

	7.7 Adaptit Activities Completed 7.7.1 7.7.1.1 WP2 • Installation and maintenance of a local Big Data testbed (Hadoop cluster) in the Google cloud. • Deliverable D2.1 (Requirements strategy): Documenting the requirements on the Big Data architecture. Editing and contributions. Has been completed since M4. • Deliverable D2.2 (ONTIC Big Data Architecture): Contributions on the Big Data architecture (storage, processing components). Editing of the deliverable. Completed in M12 Activities in Progress 7.7.2 7.7.2.1 WP2 7.7.2.2 WP3 • Study of offline techniques for traffic classification according to multiple, dynamically-defined, criteria. • Study of offline techniques for calculating time-varying usage metrics of classified traffic in a parallel computing platform. 7.7.2.3 WP4 • Study of efficient techniques for online calculation of usage metrics in a time series fashion for different traffic mixes 7.7.2.4 WP5 • Study of use cases on dynamic congestion and QoS management. 7.7.2.5 WP6 • Internal meetings with pre/sales teams for exploiting Ontic participation into commercial activities. • Plans and Actions for the Next Reporting Period 7.7.3 7.7.3.1 WP2 • Deployment and configuration of the project Big Data platform in our local testbed; execution of validation and performance tests. • 619633 ONTIC. Deliverable 1.1
	34 / 36

• Deliverable D.2.4 (Provision subsystem capabilities): Provisioning system capabilities; editing of and contributions on Deliverable D2.4 completed in M12 7.7.1.2 WP4 • Review of Deliverable D.4.1. 7.7.1.3 WP5 • Contribution on SotA for use case 3 on dynamic QoS provisioning. 7.7.1.4 WP6 • •

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ADVISED OF THE POSSIBILITY OF SUCH DAMAGE 619633 ONTIC

 The set of activities include scientific papers, journals and conferences of interest, IPR, press releases, and a list of relevant industrial associations that are interested in the project activities and outcomes. Both future and current to-date activities are presented, targeting different scientific communities, students, stakeholders and decision makers.

List of tables

Suggested previous readings

The contents of this document are the development of the exploitation strategy, the exploitation plans and the dissemination plans. Those plans and strategy have been described in the following documents and therefore its reading is recommended: § ONTIC. Deliverable D6.

UPM scientific dissemination

Regarding to scientific dissemination, the following list represents the accepted papers presented by UPM in the second period: To promoting the dissemination of the work activities and results of the ONTIC project, the specific goals of BigDAP 2015 were:

• B.
• To provide a networking space for people in academic institutions and in the industry.

• To enhance the participation of graduate students, especially those enrolled in PhD programs related to Big Data, Big Analytics and Network Science.

The intended audience of the workshop can be grouped as follows:

• Industrial sector: professionals involved in R&D&I (Research, Development and Innovation) activities. • Enterprise sector: representatives of SMEs and large corporations interested in improving their organizational structure and business intelligence by leveraging novel Big Data technologies and analytics. • Professors and researchers whose research interests focus on activities carried out by ONTIC. • PhD students who are working on a thesis related to the ONTIC activities.

BigDAP 2015 consisted of one track that gathered both industrial and academics perspectives. The sessions were structured in three parts separated by breaks. The first one featured an invited talk and one paper presentation, while the other two sessions featured 4 paper presentations each.

The topics of interest of BigDAP 2015 included but not limited to the following: Changing from the previous year, the program was structured in a single block featuring all contributions and one invited talk. The program was divided into three sessions.

• Invited Talk: Computational Urban "Science''. Dr. Daniele Quercia • Accepted papers: 12 papers were submitted, of which 9 of them were accepted.

The BigDAP website offered a compilation of the information about the CFP, the accepted papers and the program of the workshop.

In the manner of the ONTIC portal, the BigDAP website contains links to the communication channels for diffusion and feedback (linking from/to the project's website and the project's twitter page)

The website can also be accessed directly through the following URL:

http://dbdmg.polito.it/bigdap2015/ as shown in Figure 1. BigDAP 2015 web page.

Figure 1. BigDAP 2015 web page

To promote the dissemination of the Workshop, several different channels were used:

• Via email, using international email distribution lists from academic fields related to Computer Science in Europe.

• Via email, sent by the Programme and Organization Committee members to personal contacts in academic and industrial fields.

• Using webpages and other internal or external dissemination means available in each of the consortium partners.

• Via the ONTIC project webpages, through the 'Event' and 'News' sections.

• Via twitter, using the official ONTIC project twitter.

The workshop proceedings have been generated by Springer in both digital and physical formats, and are available at http://www.springer.com/us/book/9783319232003

We estimate that about 50 persons attended the BigDAP Workshop; there is no official data disclosed by the ADBIS organization about the number of attendees at the main conference or at specific workshops.

ONTIC successfully organized the second BigDAP Workshop in co-location with ADBIS, 2015.

The following summarize the main outcome of the event:

• Presentation and dissemination of the state of the art in different areas of research and development in the field of Big Data, both in the academic and the industrial worlds. The level of participation was satisfactory given that BigDAP is still young.

• Networking: New contacts have been established with people connected to the industrial and academic and/or scientific fields, achieving a useful interexchange of ideas and knowledge.

• Visibility: The ONTIC project work and results were made known to a wider industrial and scientific community.

• Encouragement of undergraduate and postgraduate students, especially PhD students, to get involved in research fields related to Big Data, Big Analytics and Computer Science.

• Produced a proceedings volume published by Springer, both in digital and physical format, with an associated ISBN.

Public Availability of the ONTS dataset

The ONTIC project had previously planned to make publicly available the ONTS dataset at the end of the project. As project reviewers recommended to publish it as soon as possible, we announced the public availability of the ONTS dataset on December 2015 via the ONTIC website and twitter. Details of this process can be found in Deliverable 2.5 (Progress on the Provisioning Subsystem), section 8. We also contacted with FP7 CSA Rethink-Big Project (http://www.rethinkbig-project.eu) to help us in the dissemination of this event. In addition, we plan to publish a small portion of ONTS in the University California Irving (UCI) Machine learning repository web site (https://archive.ics.uci.edu/ml/datasets.html) in the next weeks. UCI repository is a wellknown dataset repository that is very popular among machine learning and data mining researchers, and so, it is a strategic place in order to amplify the announcement of this event.

Other activities

SATEC dissemination activities

In order to present the ONTIC project, partials results and possible commercial uses SATEC has made the following activities and actions:

• Big Data internal workshop for Business Development and sales department.

• Participation in the SATEC Big Data and Analytics internal task force.

• Adding ONTIC references in the customer proposals.

EMC dissemination activities

Several activities developed to create awareness of the ONTIC project inside EMC, focus in sales people as they are the right people to reach out our customers and partners:

• Quarterly sessions organized by Professional Services -EMC Spain, to explain sales force the kind of projects delivery team is working on. ONTIC project has been included as one of the Big Data references to tell our customers, to identify possible candidates interested in ONTIC goals and progress.

• As part of a global Big Data practice, we have worked at EMEA level to explain and give visibility of this project to another countries in the region; for that purpose, we have added the ONTIC project to our EMC Delivery Portal (Polaris web portal), that it´s the first access point to get any project information inside the EMC federation (Figure 2):

ADAPTIT dissemination activities

Adaptit has participated in the Mobile World Congress, Barcelona, February 2015, and presented the ONTIC project to visitors in its stand.

Continuing its dissemination activities from first year, the vision and work activities of ONTIC and the particular involvement of the company have been presented to a number of customers, including service, network providers and academia. These presentations happened during business meetings for showing the commitment and know-how of the company in the area of Big Data infrastructure solutions and analytics.

UPM dissemination activities

Other activities by UPM are listed in the following list:

• European Data Forum 2015 Sponsorship: UPM was one of the Silver Sponsors of the European Data Forum (EDF) 2015 (http://2015.data-forum.eu/) that took place on November 16-17, 2015 in Luxembourg.

The European Data Forum (EDF) is a meeting for industry professionals, researchers, policymakers and members of community initiatives to discuss the challenges of Big Data and the emerging Data Economy and to develop suitable action plans for addressing these challenges. Of special focus for the EDF are Small and Medium-sized Enterprises (SMEs), since they are driving innovation and competition in many data-driven economic sectors.

The topics discussed at the European Data Forum range from novel data-driven business models and technological innovations to societal aspects.

Next year, we plan not only to sponsor this relevant event but also to show its main results and specifically the three use case prototypes. Polito plans to iterate on this activities in the next year, updating the contents an improving the dissemination based on the feedbacks of the audience.

CNRS dissemination activities

Philippe Owezarski has been involved in the design and set-up of a new joint specialty in the engineering schools and university of the city of Toulouse. This new joint specialty, called TLS-SEC (Toulouse Security) is related to the teaching of all aspects of security aspects in networking and software engineering. It includes a lecture given by Philippe Owezarski on network anomaly detection in high bandwidth networks. It includes the data mining and machine learning algorithms that have been designed in the framework of the ONTIC project, the documentation provided to students including ONTIC related papers.

CNRS also promoted the ONTIC project to academic and industrial colleagues that we had the opportunity to meet. It was successful with the BORDER6 company that signed a contract with CNRS about the UNADA technology transfer. We are also still negotiating a contract with Airbus for transferring them our anomaly detection algorithms in big data environments.

619633 ONTIC. Deliverable 6.5 20 / 27 ! !

Exploitation Activities During Second Period

Ericsson exploitation activities during the second period

Ericsson contribution to ONTIC is focused on the so-called Quality of Experience use case. Therefore, it focuses on the Automated Management area (sometimes referred to as autonomic networks). The ambition to increase the degree of automation in a telecommunication network is not new. Autonomic networks have been a subject of research over the last 15 or so years, and technologies for increasing the workflow automation of getting business offerings to market has also been evolving. Recently, however, there have been advances in a few specific technologies that increase both the urgency to address automation as well as the possibilities to do so. These trends in themselves are further enabled by the possibilities of new computational, memory and storage capabilities.

The exploitation activities related to ONTIC carried out by the Ericsson are two-fold: on one hand, increasing awareness outside our company; on the other, addressing key stakeholders within the company in order to get the ONTIC findings and deliverables to the actual product evolution.

With regard to external awareness Ericsson has pursued to provide the means of the understanding of the scenario it is being addressed. "Adaptive Quality of Experience: A Novel Approach to Real-Time Big Data Analysis in Core Networks" [START_REF] Bascuñana | Adaptive Quality of Experience: A Novel Approach to Real-Time Big Data Analysis in Core Networks[END_REF] from the proceedings of the Second International Workshop BigDAP '15 has focused on describing the possibilities of actuation in telecommunication networks to mitigate situation of degraded QoE, by using the PCC architecture with ONTIC add-ons. it was introduced as part of the topics for discussion and got useful insights from operators and partners: how accurate QoE degradation predictions can be, how far in advance insights can be generated, which are the better policies for mitigating QoE degradation, and how fine-grained actuation can be.

In addition to that, members of the Ericsson ONTIC team have contacted the customer units in charge of both the analytics and customer experience products and services. The Adaptive QoE Control scenario is part of their innovation folder.

Figure 6. Adaptive Quality of Experience as link between EEA (QoE degradation prediction) and SAPC (policy decision)

For achieving successful exploitation, the ONTIC findings and deliverables have to be fed to the product organizations. Regarding internal activities, we can mention the contribution to the Ericsson COMPA architectural model. COMPA stands for Control/Orchestration/Management/Policy/Analytics and is an architecture concept with a set of guiding principles to simplify the operations (both management and business process) of the telecom networks [START_REF] Rune | Architecture evolution for automation and network programmability[END_REF] § Policy is a function that governs the choices in behavior of a system.

§ Management is the function that, with full lifecycles, coordinates the efforts to accomplish goals and objectives using available resources efficiently and effectively.

SAPC (PCRF) Policy Enforce. Monitoring

QoE Degradation Prediction

ADAPTIVE QoE CONTROL

QoE degradations prevented or alleviated, timely and without human intervention Predicted Degradations

Adaptive Policy Control § Control is the functionality responsible for negotiating, establishing, maintaining and terminating dynamic data/user plane connections. Data that are changed by a control function will normally be lost in case of an outage. § Orchestration describes the automated arrangement, coordination, and (resource) management of complex computer systems, middleware, and services (including networking). § Analytics is in charge of turning data into information and insights that serve as a basis for decision making and triggering of actions.

Exposing network analytics insights enables the definition of network KPIs. A policy engine can use network analytics to check KPIs, triggering network updates when needed. Such requests could then be applied to the relevant network elements by the controlorchestration-management. A closed feedback loop, the so-called COMPA loop, from the control-orchestration-management functionality back to the policy engine would enable policies to learn and adapt automatically as the network environment changes. The Adaptive QoE scenario fits very well into this model and therefore its architectural findings have been fed to the COMPA architecture (Figure 7) so that it can benefit from real use cases that help to analyze its feasibility. In particular, the Analytics Function modelled in the Adaptive QoE scenario has been modelled as the "A" in COMPA while the functionalities assigned to the Policy Governance Module have been mapped to the "P" in COMPA.

To sum up, Ericsson exploitation activities are aimed to get the adoption of the policy functionalities described and developed within the Adaptive QoE Control scenario. On one hand, Ericsson has carried out diffusion and pre-commercial activities. On the other, has guaranteed that the adoption by the Ericsson product offering and the coherence with the architectural proposals for autonomic networks within the 5G umbrella.

SATEC exploitation activities during the second period

The main focus of SATEC in the second period has been centered in the development of the first version of the analytic dashboard which is defined as part of use case #1. This has implied the definition and implementation (integration of several modules, programming libraries and tools) of a realistic Big Data architecture, the functional definition of the analytic dashboard for data network analysis, and eventually the implementation of the dashboard as a first proof of concept.

All that work has provided to SATEC a good expertise in Big Data and scalability which has been moved to the marked in two different groups of customers: telco customer and non telco customers.

All that work has provided to SATEC a good expertise in Big Data and scalability which has been moved to the marked in two different groups of customers: telco customer and non telco customers.

The SATEC exploitation activities in the group of telco customers has been: § Demonstrations on real time dashboard for network administrators. § Software reengineering for NSIM product (currently installed in production in a very big international telco company). SATEC is moving current technical architecture to a Big Data one with the same components used in the Dashboard for ONTIC use case #1 § Proposals to real time software monitoring systems in data networks for telecom operators and ISPs

During the second period SATEC has contacted, has attended meetings and has sent commercial proposals (all about the technical bases enumerated above) with several costumers such as: § TIWS (Telefonica International Wholesale Services).

§ Vodafone § Orange.

The activity in the group of non-telco customers has been targeted to the utilities, mainly electricity, gas and water), and specifically in the environment of Smart Grids.

In this area SATEC is developing proposals of services based in the consumption analysis, power balance, etc. in Smart Grids. SATEC is defining services that will be based in the ONTIC areas such as: feature selection (managing of variety of smart grid data), scalability (Big Data architecture to support the input of millions data from smart grid meters), data clustering, etc. Some actual proposals in progress by SATEC are: § Electricity consumption characterization tool.

§ A system of expert support analysis for Smart Grid. § SLAs verification system for gas transportation

During the second period SATEC has contacted, has attended meetings and has sent commercial proposals with the following non-telco costumers: § Endesa. § Gas Natural-Fenosa. § Repsol.

EMC exploitation activities during the second period

During this second year we have been working closer with EMC sale forces to identify possible candidates interested in ONTIC goals and progresses. As ONTIC domain is focus on network traffic, our primary interest is in Telco companies, but we are trying also to explain this project to others companies as it can be used as a powerful reference to convince them about the benefits of adopting Big Data technologies.

We have seen also a special interest in one of the Use Cases we are working in ONTIC project, related with Video User Experience. There is an interest in improving the user experience of their customers when visualizing videos from the internet, and for this purpose, they are looking for ways to have more control of this, so any tool they could have to anticipate possible degradation in this service will be of interest for them.

In any case, this third year of ONTIC project will be critical for the success of any initiative we can have with our customers, as one of the first things they demand is a demonstration of the ONTIC capabilities and functionality; the sooner we have something to show to our customers, the easier will be to convince our customers how useful can be ONTIC outcome for them.

Adaptit exploitation activities during the second period

ADAPTIT is interested in ONTIC project results from the view-point of a solution provider.

In particular, ADAPTIT is interested in the design and deployment of the project Big Data architecture for traffic characterization, its provisioning through the cloud and the analytics for extracting network and application performance insights and metrics.

ADAPTIT is committed to capitalizing on the experience gained from its involvement in the project and on the collaboration with other partners of the consortium.

As set from the first year, the exploitation of project results spans two directions:

Strengthen the company's position in the market: The participation of ADAPTIT in ONTIC is exploited as a competitive advantage of the company for strengthening its position as a provider of infrastructure and performance management solutions.

The offering of Big Data solutions and analytics for performance management constitute areas of primary concern to the company and its involvement in the project (cloud-based Big Data set-up, analytics development and visualization) has strengthened its know-how in these areas. Relevant external dissemination activities for passing the company's knowhow message are on-going.

Utilize the gained know-how and experience in innovative service offerings: Based on the acquired knowledge, ADAPTIT plans to further develop their Big Data analytics portfolio for infrastructure and performance management. Big Data-as-a-service is opted as a promising sales form. The strategy is to pursue FOA (First Office Application) for exhibiting the benefits of the proposed services, with ADAPTIT undertaking the cost of the pilot project.

To this direction, ADAPTIT has progressed during this year a business case, initiated from the first year, with First Data Hellas (www.firstdata.com/en_gr/home.html/) a branch of an international application service provider that acts as a 'centre of excellence' in infrastructure management. The case is concerned with the development and visualization of Big Data analytics for cost and revenue analysis and forecasting based on usage measurements. Evidently, this case is directly aligned with the activities of the company in ONTIC. A pilot project has been defined and a prototype has been developed (August-November 2015) and demonstrated to senior technical staff. Following positive feedback, a demonstration to the executive management level is scheduled by the end of 2015.

Furthermore, presales and technical teams have been formed for undertaking direct customer contacts, presentations and pilot projects, targeting at customers in the EMEA region. Building on the business case with First Data, a meeting with Cosmote Greece, nowadays a convergent network (fixed and mobile) service provider, part of the Deutsche Telecom Group, is in the agenda within H1 of 2016. to design and deploy innovative machine learning algorithms in the context of network management in 5G networks. This proposal will be submitted to the Spanish Ministry of Economy in the next few months. § JOT Internet: This Spanish SME develops and implements advanced media advertisement solutions. We are planning to jointly explore the application of analytics in Big Data media scenarios using Spark and Storm as Big Data frameworks during the next year. § Enertika is a Spanish SME that designs and installs air conditioning solutions for telecom antenna towers. We have started preliminary contacts to collaborate in the development of advanced Machine Learning models to design and implement energy efficient solutions for these antenna towers.

UPM exploitation activities during the second period

CNRS exploitation activities during the second period

CNRS as an academic organization has to bridge the gap for its theoretical and applied research results with educational organizations (universities, engineering schools, …) on one side, and companies on the other side Dealing with educational schools, CNRS is now contributing in a new educational engineering diploma shared between the main engineering schools in Toulouse (INSA, INP, ENAC, and ISAE) and the University of Toulouse. This new diploma aims at educating students with all security aspects of the digital world. Philippe Owezarski gives a course about the analysis of network traffic in order to detect attacks and anomalies, malicious behaviors, bad configurations, etc. He especially disseminates the results gain in ONTIC on WP4 and Use Case 1 about online traffic analysis and anomaly detection. This new diploma (started in September 2015) is called TLS-SEC (for TouLouSe -SECurity).

Dealing with companies, results gained in ONTIC WP4 and Use Case 1 interested a French Company called BORDER6. BORDER6 aims at proposing network access devices that analyze the inbound and outbound traffic in real time in order to optimize routing path and QoS enforcement for traffic. BORDER6 is strongly interested by our iterative version of the UNADA algorithm (IUNADA) that was proposed in WP4 during second reporting period. They expect significant performance improvements with this algorithm, as well as the integration of security capabilities as IUNADA is aimed at detecting anomalies and attacks in the traffic. BORDER6 and CNRS signed a research contract in May 2015 for a three year period that aims at adapting IUNADA for BORDER6 devices, adding security functionality, and transferring afterwards the technical skills of CNRS to BORDER6.

POLITO exploitation activities during the second period

POLITO exploitation plans are to allow the technology and competence transfer to industry, by using ONTIC outcomes to increase awareness among industry and professionals. Furthermore Polito plans to contribute with publications of the scientific results in journals such as IEEE Transactions On Knowledge and Data Engineering and ACM Transactions on Knowledge Discovery, and specific purpose conferences such as IEEE BIG DATA, then use ONTIC as base platform for research on novel Big Data algorithms and data mining techniques in H2020 or national projects (e.g., PRIN), and let students exploit ONTIC activities for completing their Degree Final Project.

Elena Baralis, WP3 leader, has joined the scientific and steering committee of the Master degree in Big Data Engineering by Politecnico di Torino, where most participants come from international companies. The course content has taken advantage from the Big Data experience gained during ONTIC activities, and allowed the Polito team to establish relationships with relevant companies.

Figure 1 .

 1 Figure 1. BigDAP 2015 web page .................................................................. 14 Figure 2. EMC Polaris portal: ONTIC project .................................................... 16 Figure 3. Sponsors page featured on the EDF web site (http://2015.data-forum.eu/) .. 17Figure 4. Page 30 of the EDF 2015 Programme. ................................................ 17 Figure 5. Adaptive Quality of Experience high-level architecture ........................... 20 Figure 6. Adaptive Quality of Experience as link between EEA (QoE degradation prediction) and SAPC (policy decision) ........................................................... 21 Figure 7. COMPA Architecture applied to Mobile Packet Core Networks ................... 22

Figure 4 .

 4 Figure 1. BigDAP 2015 web page .................................................................. 14 Figure 2. EMC Polaris portal: ONTIC project .................................................... 16 Figure 3. Sponsors page featured on the EDF web site (http://2015.data-forum.eu/) .. 17Figure 4. Page 30 of the EDF 2015 Programme. ................................................ 17 Figure 5. Adaptive Quality of Experience high-level architecture ........................... 20 Figure 6. Adaptive Quality of Experience as link between EEA (QoE degradation prediction) and SAPC (policy decision) ........................................................... 21 Figure 7. COMPA Architecture applied to Mobile Packet Core Networks ................... 22

•

  Big Data applications • Case studies on Big Data management and analytics • Experiences with Big Data project development and deployment • Big Data models, algorithms, and architectures • Cloud computing techniques for Big Data • Big Data management • Programming models and environments to support Big Data • Parallel, distributed computing and virtualization for Big Data • Big Data frameworks and architectures • Big Data integrity and privacy • Big Data search and mining • Big Data analytics and visualization/ Scalable Data Mining and Machine learning techniques • Research challenges on Big Data management and analyticsThe calendar for the event was established by the Programme Committee, as defined in the following table:

Figure 2 .

 2 Figure 2. EMC Polaris portal: ONTIC project

Figure 3 .Figure 4 .

 34 Figure 3. Sponsors page featured on the EDF web site (http://2015.data-forum.eu/)

Figure 5 .

 5 Figure 5. Adaptive Quality of Experience high-level architecture

Figure 7 .

 7 Figure 7. COMPA Architecture applied to Mobile Packet Core Networks

Table of Contents

 of Conferences, workshops and journals .................................................. 10 4.1.1 POLITO scientific dissemination ............................................................ 10 4.1.2 UPM scientific dissemination ............................................................... 11 4.1.3 CNRS scientific dissemination .............................................................. 12

	1. ACRONYMS AND DEFINITIONS	7
	2. EXECUTIVE SUMMARY	8
	3. SUGGESTED PREVIOUS READINGS	9
	4. DISSEMINATION ACTIVITIES DURING SECOND PERIOD	10
	4.1

4.2 BigDAP2015 workshop .................................................................... 12 4.3 Public Availability of the ONTS dataset ................................................ 15 4.4 Other activities ............................................................................ 15 4.4.1 SATEC dissemination activities ............................................................. 15 4.4.2 EMC dissemination activities ...............................................................

Table 1 :

 1

Acronyms ..................................................................................... 7 Table 2: BigDAP 2015 Important Dates ........................................................... 13 1. Acronyms

and Definitions AcronymsTable 1 :

 1 Acronyms

	Acronym Defined as
	ACM	Association for Computing Machinery
	ADBIS	Advances in Databases and Information Systems
	BigDAP	Big Data Applications and Principles
	EDF	European Data Forum
	ICDM	International Conference on Data Mining
	IEEE	Institute of Electrical and Electronics Engineers
	ISBN	International Standard Book Number
	IPR	intellectual property rights
	ISP	Internet Service Provider
	KPI	Key Performance Indicator
	NOMS	Network Operations and Management Symposium
	ONTIC	Online Network TraffIc Characterization
	QoE	Quality of Experience
	QoS	Quality of Service
	SAPC	smart Policy Control
	SLA	Service Level Agreement
	R&D&I	Research and Development and Innovation

of Scalable Approaches for Frequent Itemset Mining. 19

  Specifically addressing scientific dissemination, currently published conference papers are listed below: § Elena Baralis, Luca Cagliero, Paolo Garza, Luigi Grimaudo.th East-European Conference on Advances in Databases and Information Systems (ADBIS), 2nd International Workshop on Big Data Applications and Principles (BigDap 2015), September 8-11, 2015, Futuroscope, Poitiers, pp: 243-247. § Daniele Apiletti, Elena Baralis, Tania Cerquitelli, Paolo Garza, Luca Venturini.

	PaWI: Parallel Weighted Itemset Mining by Means of MapReduce.
	IEEE International Congress on Big Data (BigData Congress),
	New York, NY, June 27-July 2, 2015, pp: 25-32, ISBN: 978-1-4673-7277-0
	http://dx.doi.org/10.1109/BigDataCongress.2015.14
	§ Daniele Apiletti, Elena Baralis, Tania Cerquitelli, Paolo Garza, Fabio Pulvirenti, and
	Pietro Michiardi.

[START_REF] Bascuñana | Adaptive Quality of Experience: A Novel Approach to Real-Time Big Data Analysis in Core Networks[END_REF]

. "Exploitation Strategy". May 2014.

[START_REF] Ontic | Deliverable D6.3. Exploitation Strategy[END_REF]

§ ONTIC. Deliverable D6.4, "Exploitation and Dissemination Plans". January 2015.

[START_REF] Ontic | Deliverable D6.4. Progress on Exploitation and Dissemination Plans -Part I[END_REF]

4. Dissemination Activities During Second Period 4.1 Conferences, workshops and journals 4.1.1 POLITO scientific dissemination PaMPa-HD: a Parallel MapReduce-based frequent Pattern miner for High-Dimensional data. IEEE International Conference on Data Mining (IEEE ICDM), 3rd International Workshop on High Dimensional Data Mining (HDM'15), November 14-17, Atlantic City (New Jersey, USA). § Daniele Apiletti, Paolo Garza, Fabio Pulvirenti.

A Review

SaFe-NeC: a Scalable and Flexible system for Network data Characterization.

	IEEE/IFIP Network Operations and Management Symposium (IEEE NOMS), Istanbul,
	Turkey, 25-29 April 2016.
	Furthermore, two additional papers have been prepared; they are under submission to ISI-
	indexed journals:
	• A review of state-of-the-art distributed Frequent Itemset Mining algorithms, with
	real-world experimental sessions, by Daniele Apiletti, Elena Baralis, Paolo Garza, and
	Fabio Pulvirenti

• A review of state-of-the-art distributed clustering algorithms, with real-world experimental sessions, as a joint work between Polito and UPM research teams.

Parallel subspace clustering algorithm on Spark 19th East-European Conference on Advances in Databases and Information

	• B. Zhu, B. Ordozgoiti, A. Mozo
	PSCEG: An unbiased Parallel Subspace Clustering algorithm using Exact Grids.
	24 th European Symposium on Artificial Neural Networks, Computational Intelligence
	and Machine Learning ESSAN16, Bruges (Belgium), 27 -29 April 2016. ESSAN is rated
	as Core B.
	• B. Ordozgoiti, S. Gomez, A. Mozo.
	Massively Parallel Unsupervised Feature Selection on Spark.
	19th East-European Conference on Advances in Databases and Information Systems
	(ADBIS), 2nd International Workshop on Big Data Applications and Principles (BigDap
	2015), September 8-11, 2015, Futuroscope, Poitiers.
	This work was selected as one of the best papers in BigDAP15 and consequently
	proposed by the ADBIS Chair for its submission in extended form to a special issue of
	the Information Systems Frontiers Journal (JCR indexed journal).
	• B. Zhu, A. Mara, A. Mozo
	CLUS: Systems
	(ADBIS), 2nd International Workshop on Big Data Applications and Principles (BigDap
	2015), September 8-11, 2015, Futuroscope, Poitiers.
	• B. Ordozgoiti, S. Gomez, A. Mozo
	NPEPE:

Ordozgoiti, S. Gomez, A. Mozo Parallelized Unsupervised Feature Selection for Large-Scale Network Traffic Analysis. 24 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning ESSAN16, Bruges (Belgium), 27 -29 April 2016. ESSAN is rated as Core B.

Massive Natural Computing Engine for Optimally Solving NP-complete Problems in Big Data Scenarios 19th

	East-European Conference on Advances in Databases and Information Systems
	(ADBIS), 2nd International Workshop on Big Data Applications and Principles (BigDap
	2015), September 8-11, 2015, Futuroscope, Poitiers.
	The following list includes submitted (under review) Papers:
	• B. Ordozgoiti, S. Gomez, A. Mozo
	Probabilistic

Leverage Scores for Parallel Unsupervised Feature Selection on Massive High-Dimensional Datasets.

	4.1.3
	SIAM International Conference on Data Mining SDM16. SIAM-SDM is rated as Core A.
	• A review of state-of-the-art distributed clustering algorithms, with real-world
	experimental sessions, as a joint work between Polito and UPM research teams.

CNRS scientific dissemination Regarding

  to scientific dissemination CNRS has presented the following paper in the second period:• Juliette Dromard, Gilles Roudière and Philippe Owezarski.

Unsupervised Network Anomaly Detection in Real-time on Big Data.

	19th East-European Conference on Advances in Databases and Information Systems
	(ADBIS), 2nd International Workshop on Big Data Applications and Principles (BigDap
	2015), September 8-11, 2015, Futuroscope, Poitiers
	4.2 BigDAP2015 workshop
	The Second International Workshop on Big Data Applications and Principles, BigDAP 2015, is
	the second Workshop sponsored by ONTIC. The key topics of the Workshop included the
	application of scalable Big Data analytics to network traffic characterization, the main
	theme of ONTIC, as well as to other application domains.
	For enhancing visibility and attendance, BigDAP 2015 was co-located with the 19th East-
	European Conference on Advances in Databases and Information Systems (ADBIS 2015) at
	Poitiers, France. ADBIS is a Core-B conference. The workshop took place on Tuesday,
	September 8, 2015 with 10 presentations, including an invited talk and arranged into two
	sequential tracks: a scientific and an industrial track. Half of the presentations came from
	external authors, while attendance was definitely satisfactory.

Table 2 : BigDAP 2015 Important Dates Important Dates

 2

	April 17 th , 2015	Deadline for paper submission
	May 3 rd , 2015:	Deadline for paper submission (EXTENDED)
	May 29 th , 2015:	Notification of paper acceptance or rejection
	June 12 th , 2015:	Camera-Ready Submission
	June 22 nd , 2015:	Camera-Ready Submission (EXTENDED)
	June 8 th , 2015	Early registration
	July 5 th , 2015	Late registration

•

  Academic dissemination: UPM presented several ONTIC activities and outcomes at the following events: o Lecture on Machine Learning algorithms applied to telecom scenarios (2015), included in the subject "Network Science" (Master of Computer Sciences and Technologies) and taught at UPM in November 2015. o Lecture on Spark and Hadoop in the subject "Internet Services, Protocols and Applications" (Master of Distributed and Embedded Systems) and taught at UPM in October 2015. o Professor Alberto Mozo is mentoring three PhD candidates (two of them are part of the ONTIC team and the other is an intern sponsored by Ericsson Spain) in research topics closely aligned with ONTIC research activities.

4.4.5 POLITO dissemination activities

Daniele Apiletti and Elena Baralis held the second edition of a seminar on ONTIC technologies in the undergraduate course of "Database Management Systems" for the Computer Engineering students al Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy, in December 2015. Recent advances in Big Data technologies related to ONTIC activities were presented to an audience of approximately 200 students enrolled for the course. Furthermore, prof. Elena Baralis introduced ONTIC activities into her PhD course at the Dipartimento di Automatica e Informatica, Politecnico di Torino, held from mid-September to mid-October 2015 (10 lessons overall). ONTIC concepts and research activities were spread across different lessons covering clustering techniques, classification models and correlation analysis by means of association rule mining. The audience included approximately 20 PhD students, mostly coming from the Computer and Control Department. Finally, Paolo Garza in Academic Year 2015-2016 is holding a new Master-degree course in Big Data technologies and techniques within the Computer Engineering degree. Contents and lessons have been greatly influenced by ONTIC research activities.

  So, communication and cooperation between the Ericsson Expert Analytics and Ericsson Service-Aware Policy Control product organizations have been brokered and support to cooperation activities has been provided. The first results were obtained in 2015, just in order to go to the market and get proper feedback: Dynamic Experience Management is a feature that has been tested and can be ordered by Ericsson customers. The insights provided by Ericsson Expert Analytics (EEA) on individual Customer Quality of Experience blended with smart Policy Control (SAPC) [5] allow real time network adaptation to solve identified service degradations for the greatest revenue producing, most valuable customers (Figure6). Assuring a great application experience for these VIP customers creates value for both, the service consumers and the Mobile Broadband operators. This feature was showcased in the Ericsson Innovation Roadshow which was held in Madrid on October 20-22, 2015[6]

  Collaborations with Industrial partners made by UPM are listed below: § IBM Dublin Software Lab: We have contacted members of this Lab with a special research interest in Applied Predictive Modeling, Machine Learning, Data Analytics, Big Data and Pervasive Computing. They would like to share some datasets with us to exploit them in the context of online network traffic classification and prediction. In addition, IBM declared several months ago that they are going to officially support Spark. Therefore, we plan to exchange with them know-how for the development of efficient and scalable machine learning algorithms on top of Spark. § Bell Labs Israel: This prestigious research institute contacted us to express their interest in exploring a joint collaboration in the application of Machine Learning and Data Mining models to energy efficiency and performance in Network Function Virtualization (NFV) scenarios. § Telefonica: UPM has been invited to join an ambitious research proposal lead by Telefonica

/ 27 ! !