Hamza Reffad
email: reffadh@yahoo.fr

Adel Alti
email: alti.adel@univ-setif.dz

Philippe Roose
email: philippe.roose@univ-pau.fr

Cloud-based Semantic Platform for Dynamic Management of Context-aware mobile ERP applications

Keywords: CCS Concepts, Distributed architectures ➝ Cloud computing., Enterprise computing ➝ Enterprise information systems ➝ Enterprise resource planning Context-aware, semantic virtual business services, Cloud, ERP, QoS, multi-enterprises profiles

Mobile cloud computing (MCC) has attracted more and more attention from both Small and Medium Enterprises (SMEs). MCC provides access to cloud services for mobile devices. With the growing popularity of smart mobile devices, customers need efficient and new high quality services. In this context, mobile ERP services management and adaptation at real time are becoming a challenging task. However, it is necessary to handle in a dynamic intelligent and transparent platform various mobile cloud services provided by multiple cloud providers with several qualities. This paper presents CloudCxQERP platform, which is a cloud-based semantic platform for dynamic management of context-aware mobile ERP applications. It is based on semantic web technologies and cloud computing and provides fast ERP development process and "an ecosystem" of enterprises that ensures quality customer service. Moreover, our platform provides a middleware that offers to users more efficient services corresponding to current situations and adapts its ERP applications more quickly and cost effectively. Experiments are based on online packaging boxes services.

INTRODUCTION

Mobile cloud computing (MCC) has attracted more and more attention from both Small and Medium Enterprises (SMEs). MCC is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices such as computational resources, computing utilities, scalability. Today, the massive use of new technologies has led to a dramatic multiplication of a wide range of ERP business services, configurations, redundant context and enterprises profiles. Moreover, there is an increasing usage of heterogeneous mobile devices as well as various customers searching efficient and new high quality interactive services. This could imply that each company needs to manage business processes on various devices. In many cases, this situation could overload a provider. Developing and managing different mobile cloud services for providers are costly. For instance, when a given purchasing service is provided by two or more providers, one company may want a fast transaction time, while another one may want a better price without extra travel and custom fees; which one improve company's satisfaction with relevant service among the provided services.

The objective of this work is to present cloud-based semantic platform called CloudCxQERP. CloudCxQERP allows a dynamic semantic management of context-aware mobile ERP applications in order to handle various mobile cloud services provided by multiple cloud providers with several qualities. It provides also more helpful to customers to intelligently provide up the right services to current needs and to current situations (devices, location, etc.). A platform for achieving this objective rests on three key elements: first, of course, there has to be an "ecosystem" of enterprises/customers profiles that semantically grouped according to their functional and contextual descriptions. Second, there has to be inference engine constraints analytics to intelligently match up current constraints with cloud semantic services, and finally there is the underlying technology to ensure the deployment or calls services are as efficient and high quality as possible. A platform needs to provide a middleware that offers to users more efficient services corresponding to their current situations and adapts its ERP applications more quickly and cost effectively. The proposed platform is built according to three primary layers. The first generic semantic core layer is a middleware itself and that sits in the Cloud. It provides the semantic reasoning and clustering facilities. Above a middleware, a platform accumulates a network of enterprises, costumers and on-line business affairs between both participants.

The rest of this paper organized as follows: Section 2 presents related works. Section 3 details ontology model. Section 4 describes our adaptation platform architecture, i.e., the CloudCxQERP platform. Section 5 validates our proposal and section 6 concludes the paper with some future works.

RELATED WORKS

In this section, we present works directly related to our proposal and point out the differences with our model. The INFOR publisher [START_REF]EuroCloud: Cloud Letter, Knowledge Base of Cloud[END_REF] provided a semantic connection between some ERPs standards (i.e. SAP Oracle and SAGE Microsoft) through its oracle fusion middleware. French publishers with 15 years of experience in Oracle Fusion Middleware, Mobile and Cloud implementations, provided enterprises with scalable and extendable ERP services, including marketing management, customer relationship and online sales. More recently, this publisher was oriented to business solutions in the Cloud "CloudSuite as full SaaS". They didn't talk exactly about cloud model adaptation of the business process to have full control of how business transactions should be received in different devices (Laptop, Tablet, Smartphone…) and nor multiparts enterprises profiles. This publisher provides richer online ERP services but did not take into account the business category semantics, multi-enterprises clustering profiles and QoS (lower implementation cost, lower time for costumer, meet most of enterprises requirements, flexibility to meet with context changes).Gronli1et al. [START_REF] Tm. Gronli1 | Automatic Configuration of Mobile Applications Using Context-Aware Cloud-Based Services[END_REF] proposed a cloud based framework for adapting mobile business services in pervasive computing environment by constructing a private mobile business service adaptation agent for each mobile user in the cloud. However, this work hasn't considered the contextual information of customers and enterprises. In addition, this work does not hold the services selection and then, it does not allow providing the appropriate service to a customer. Other works have used cloud computing paradigm as solution for the huge burden on user's mobile computing devices and overcoming obstacles related to the ERP performance. In [START_REF] Paya | Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem[END_REF] authors proposed an architecture to publish the business ecosystems of several enterprises, in the center's "cloud" for storage, processing, and distribution. In [START_REF] Mohan | Cloud Computing and Enterprise Resource Planning (ERP) Systems in Cloud Environment[END_REF] authors proposed an infrastructure called ERP-Cloud infrastructure which can manage business process on IT infrastructure. In [START_REF] Gelogo | Enterprise Resource Planning Asipacific Journal of Multimedia Services Convergence with Art[END_REF] authors proposed mobile cloud ERP based on the adaptive object model for providing business process scalability and reducing hardware costs. This work is more efficient on terms of memory usage. According to [START_REF] Satyanarayanan | The Case for VM-Based Cloudlets in Mobile Computing[END_REF][START_REF] Rekik | A decisionmaking method for business process outsourcing to the cloud based on business motivation model and AHP[END_REF], the context information is indispensable to control the complexity of enterprise systems. Therefore, an enterprise system that uses mobile computing devices which connect over-time to the cloud in more efficient manner in terms of energy, CPU and bandwidth when the enterprise suffers. All these works have proposed interesting solutions for acquiring, storing and managing business affairs using cloud computing paradigm. However, they are constructing a specific business process for each enterprise profile. Business data will be processed in the public cloud and not in private cloud, so if a security attack problem will be met, the business data cannot be in a safe status in the cloud and that leads the security of business transactions. Rekik et al. [START_REF] Rekik | Cloud Description Ontology for Service Discovery and Selection[END_REF] proposed a cloud description ontology for service discovery and selection. Cloud ontology and semantic techniques are used for modeling a generic customer requirements and contextual cloud services, which enables the system to guide a customer selecting the service that best fits his/her needs in the given context. This work is efficient and shared between different services using OWL as the best candidates to describe and format those services. However, this work uses manual constraints checking and not supports logic inference. They also not provide possibilities for using reasoning techniques. Gupta et al. [START_REF] Gupta | Misra Implementation of Cloud ERP Moderating Effect of Compliance on the Organizational Factors[END_REF] have proposed critical organizational success factors for Cloud ERP. They based on market-based view (i.e. budget of company and ERP project budget), knowledgebased view (i.e. economic strategies and implementations strategies) and social-based view (i.e. strategic goals and objectives). They included also QoS criteria like implementation cost, customers' satisfaction, etc. without using contextual information of customers and enterprises which may be as relevant as the required market-based views. Another interesting work is combination of Cloud computing paradigm and optimization techniques [START_REF] Chena | A cloud computing platform for ERP applications[END_REF] to manage resources over the across clients' access. This work based on genetic optimization method for selecting and composing best ERP services. It proposes a generic solution to manage and aggregate of quality business services from multiple providers. This work did not take into account the semantics of services and that of the category, QoS and context providers. Currently, Cloud ERP was developed with standard technologies that meet several qualities of service (reliability, safety, efficiency). However, mobility, constraints complexity and contexts of information will not be considered. This implies the following disadvantages: (1) -mobility introduces a degree of heterogeneity and highest dynamicity compared to traditional ERP systems (2) -a poor quality of service of Cloud ERP, consequence of the syntactic description without the context information and (3) -many enterprises profiles cloud has been specified, so many business process have to be computed and updated. In many cases, this situation could overload the provider. The methods and contributions of this paper are different from above related works. Our goal is looking for generic high level semantic abstraction business processes that satisfy the most contextual constraints of both enterprises and mobile customers (e.g. same business constraints with same device and environment contexts). Our approach supports deep transition from a generic approach to a more specific enterprise context (s) to deploy personalized business services. Preferences could both QoS and contextual parameters. Our architecture must be able to reason upon contextual business services that comes from different and heterogeneous providers. Therefore, our architecture uses ontology and rules-based approaches to represent and reason upon contextual information, infer the current situation, and select the quality services to the enterprise staff or a customer according to the inferred situations.

PROPOSED APPROACH

The main objective of our approach is to identify and reuse the concept of a virtual business process as combination of virtual business tasks for several mobile ERP applications. It allows also the fast identification process of relevant cloud business services and improves the efficiency and accuracy of ERP development process. These objectives are achieved through finite sets of semantic relevant cloud services. The main idea is taking the cloud as a way to group some specific business requirements and cloud services such that enterprises can share experiences, computation resources and bandwidth resources.

A mobile user prefers to adapt ERP services of specific task to his current context. Our goal is to select relevant business service that match user context with low deployment costs and to find new high-quality services to meet continuous companies' requirements and context changes.

To be efficient and shared between different cloud providers, mobile ERP applications must be as interoperable, extensible and laconic as possible. For this, ERP applications are described as OWL ontology. An overview of CxQCloudERP ontology is given in Fig. 1. It is comprised of 9 key concepts: enterprise, business process, constraints, entity (resource), context entity, QoS, cloud platform, cloud provider, cloud service. Cloud provider that provides business services  Cloud Platform Context-aware-Service is used for the deployment of several business services.

Enterprise sub-ontology

Fig. 2 illustrates the enterprise sub-ontology. The Enterprise concept is a core concept of our CxQWCloudERP ontology. It can be either a customer service enterprise or production enterprise. We propose a new concept (Virtual Enterprise) which that includes a family of enterprises. Each enterprise has functional and non functional requirements. A company has no knowledge about the published services. In addition, when a particular service is provided by two or more cloud providers in the same area of a business, a decision should be taken to select the most appropriate service based on contextual description.

Entity sub-ontology

An entity is defined as inputs/outputs of business process. We distinguish two types: physical entity (resource) and virtual entity. Physical entities are StorageResource, ComputingResource, DataResource, TaskResource, ActorResource and MonyResource. Storage resource is disks (Large, Medium, X-large, XX-large) and ram (Large, medium, large X, XX wide). A virtual entity describes all families of resources in terms of hardware or software.

Context Entity sub-ontology

We divided context sub-ontology into four essential sub-contexts:



The ExecutionContext which describes the current state of equipment (memory size, CPU load, energy, etc.). 

The QoSContext which describes the quality of any mobile-based service can be represented on our ontology, which is defined as set of metadata parameters. These QoS parameters are: 1)continuity of service, 2)speed and efficiency of service and 3)safety and security.



The HardContext is the resources (mobile devices) accessible by users such as tablets, Smart phones, etc.



The Environment Context: which describe spatial and temporal, B2B collaboration informations:

̵ Temporal information can be a date or time used as timestamp. Time is one aspect of durability so it is important to date information as soon as it is produced.

̵ The Place describes related informations about B2B collaboration {longitude and latitude}, in a given location, we can find available resources and services.

̵ The B2B collaboration: according to a B2B domain, a company can engage in B2B activity.

Business process sub-ontology

A business process is a sequence of steps to perform an identified business objective. These steps are actions carried out by different actors and synchronized through business exchanges. It would provide users with an improved services classification in one or more semantic criteria: business domain, business roles (sales, purchasing, CRM, finance, etc.), quality of service (security, deployment and execution time). We use cloud computing for semantic reasoning and clustering facilities. Our goal is to provide better use of resources in more optimum manner.

Cloud provider sub-ontology

A cloud provider relates to local and remote data centers. It is are sources provider (servers, storage and network) in terms of enhanced security, high availability, and confidentiality. Each provider provides three types of services: IaaS_Service, PaaS_Service, SaaS_Service in three modes: private, public and hybrid. When a given service is provided by two or more services providers in the same mobile client area, a decision should be taken to select the most appropriate service based on contextual description with a better quality of service. Depending on the availability of resources for the mobile device, our aim is the best service from the list of services with equivalent functionality. Specifically, in case of changes context, we dynamically switch to the next service in the list of services offering the same service functionality and requires lower resources.

Cloud service sub-ontology

The Service Cloud is made up of four sub-classes: three traditional services IaaS_Service, PaaS_Service, SaaS_Service and now virtual cloud service. The Virtualized Cloud Service allows mobile customers and staffs for improving the efficiency of a business process and hiding heterogeneity of platforms in one or more criteria: planning and scheduling strategies, federation of business partners, routing paths, business monitoring and service migration.

Constraint sub-ontology

Each context entity has its context parameters. For example, the context related to the hardware resource is a collection of parameters: machine speed, min/max of consumption. Certain context parameters may use semantically similar terms, for instance the speed of the machine and the number of units produced / minute.

The CloudCxQERP PLATFORM 4.1 The general architecture

Here is a general overview of a cloud-based semantic contextaware ERP architecture (Figure 8):



Entreprise customers: this is the device that the customer is currently using.  ERP Cloud Registry: stores the description of Cloud business services, business process patterns and ERP resources of all providers in the Cloud. When a Cloud provider is active, it sends the description of all its cloud services to the ERP proxy. Therefore, the ERP proxy has the descriptions of all services hosted on providers involved in the ERP application, and the ERP generic composition algorithm is executed. In the next section, we will detail the algorithm. When the ERP proxy receives these informations as XML files, it calls the ERP Inference Engine to cluster possible constraints of equivalent customers. This later researches for Cloud business services able to make partial or full generic services composition that satisfies most of the target customers' constraints. The algorithm gives all the possible chains satisfying the most costumers' constraints. The ERP engine selects the best chain. We use various politics based on the weight (energy saving, customer profile, low band-width, CPU charge etc.) of each chain.

Functional model of the platform

We have specified the development process that exploits our ontology in order to generate several business processes according the mobile users and their current context. This process uses a semantic inference engine that is responsible of planning and optimizing the selection, the deployment and the execution of business processes based on a customizable quality of service. In order to accelerate the development process and to better discover and compose cloud business services, we propose to enhance the expressiveness of the semantic descriptions of cloud business services, specifically to provide the "best" business service composition according to semantic properties based upon common functional and non-functional needs provided by specific companies. Our work provides a semantic description of cloud business services (e.g., name, location, role), allows a dynamic selection of cloud business services based on context metadata parameters (user, mobile device, service provider) . The process of selection and dynamic composition of ERP processes is a 7step process:

Step.1 -Business requirements clustering: The first phase is to cluster into common constraint similar enterprises based on their functional and non-functional descriptions. Therefore, the service discovery component matches set of services that share similar functionality with a most of the target enterprises needs. The clustering and classification of new services are performed offline to eliminate any overhead during service discovery. The goal is to accelerate the search process of relevant cloud business services and to reduce overall response time. The idea of the requirements clustering is to take into account the description of the functional and non-functional business requirements of two or more enterprises. Two descriptions are semantically equivalent if all the matching values between parameters are exact. We propose a clustering method based on inference rules to cluster common needs of different enterprises. If no similarity has been detected, go to step 6. Otherwise, if some similarities have been detected, it produces a common requirements guide containing common requirements. The inference of common needs of several companies expressed in SWRL as follows:

Step.2 -Business tasks semantic matching: The task manager component matches between the virtual task and the common constraint properties. The semantic matching process exploits the categorization of the task hierarchy to find suitable matches. For that purpose, it matches the virtual task properties (task category, location, time, QoS parameters, task time, inputs/outputs, etc.). Each constraint can be expressed as an AND-OR clause of a simple expression, like the following one: (taskCategory = `Buy' AND Price =`Low' AND location = `Sétif' AND TransactionType=`Internet'). The inference engine brings out set of business virtual tasks.

Step.3 -Finding relevant context-aware virtual cloud services:

After receiving the list of corresponding virtual tasks that match the most target mobile users' requirements. A cloud-based semantic service selection mechanism is based on both service semantic information and mobile users' context (e.g., less free memory, less processor power, user location and available wireless protocols) or the environment context (e.g., less bandwidth) or the mobile users' preferences. Relevant cloud business services with higher potential benefit are selected earlier and find a limited number of composition solutions to reduce the computation complexity. The following rule represent an example of inference rule which serve to answer the semantic compatibility of candidate cloud services which implements the virtual business task with the most target mobile constraints.

ComplexConstraintExpression (?cexp) ∧ ExpressionComposedOf (?cexp, ?sexp) ∧ IsCommonConstraint (?sexp, \"1\") ∧ LogicOperator(?sexp, ?logicoperator) ∧ NumericValue(?sexp, ?numvalue) ∧ SemanticValue (?sexp, ?semanticvalue) → sqwrl: selectDistinct (?cexp, ?sexp, ?parametrename, ?logicoperator, ?numvalue, ?semanticvalue

Step.4 -Generating optimized virtual business processes: This step consists in constructing all possible composition between virtual services based on the list of virtual services of Step 3. We try to find the next service by checking the services available. If the number of services is very large, at each step, we must check a lot of services. To reduce the checking time, we define the relation between a service and another one. This is based on semantic relationships between services; equivalent ≤ substitution those are used to implement rules based on their semantics. This step returns a list of composition paths.

Step.5 -Business Process refinement: Refinement is necessary to address some customization of relevant contextual parameters in order to fulfill specific needs of enterprises. For instance, one enterprise may want a fast transaction time with quality local raw materials providers, while another one may want money saving with an average quality of raw materials. The refinement component is responsible for adding/replacing specific business services for each business tasks

Step.6 -Selection of quality business processes: The goal of this step is to select a better optimal business processes from the primary process models identified in the previous step. We select the optimal business process in terms of cost with the best quality service. The process decision engine ensures the selection of the best business processes from the graph if there are several services provided by two or more cloud providers.

Step.7 -Process Execution: ERP proxy deploys cloud services and sends the result to each customer

A CASE STUDY AND EVALUATION

This section describes implementation details of our prototype. Firstly, based on OWL and SWRL languages, we have implemented our ontology and rules-based Cloud context model using Protégé tool [START_REF] Protégé | Why Protégé[END_REF]. Then, we use NetBeans IDE for development of our prototype for dynamic semantic service clustering and dynamic semantic service context monitoring.

Our prototype is based on three main parts: the first is a context monitor to simulate the semantic service behavior (price, cloud provider, execution time). The second part is contextual clustering of customers' and enterprises requirements. The last part is the dynamic business process management according to customer requirements. Our prototype generates virtual business process of both companies (AGROFILM and SYNSET) and offers to users relevant services to his current situations. Table1 illustrates requirements of a customer and Table2 shows requirements of both companies. Figure 9 shows an abstract business process diagram from a both companies' requirements and constraints of a customer. Each transaction involves five parts: a customer, order, manufacturing, a payment and a delivery gateway. The customer firstly configures it session by entering order details, browsing products informations and deciding whether to view more products information. Then the customer makes order by selecting packaging boxes models as well as filling in customer information and card credits details. The customer finally confirms the order and sends it to two companies (AGROFILM and SYNSET) and waits for a delivery response. Once the order is received, the delivery deadline is fixed for a customer and the collaborative manufacturing process started in both companies in order to fill a customer order. In parallel, companies purchase a supplier for raw materials in which the packaging boxes will be manufactured and the payment gateway verifies the credit card details. Depends on the returned status, a reply is either by email or phone call. Finally, companies deliver products to a customer in a specific location. The treatment of both companies' requirements creates an abstract business process. Some specific situations of customer (mobility of user, device resources) and/or a firm (equipment failure and no availability of raw materials) may be as relevant as the required business affairs. Several scenarios are experimented. A First scenario: a customer in a Car using a Tablet. A customer cannot read his personal emails in a Car. The Semantic Customer Context Monitor has attached a discovery protocol and GPS service as client service. When a delivery time comes, the ERP Inference Engine reinitiates the composition process, inferred actions that of discovering the available text to speech services are supported by the user location: : update Text email service with Text to Speech service. After finding the adequate services from cloud and after its selection and integration, the user can follow the email service while taking a Car. When a User asked for the fast payment, the ERP Inference Engine discovered two payment types: a credit card and a bank transfer. The Inference Engine deployed the second one because absence of security factor and unavailability of Wi-Fi.

A second scenario: delay of raw materials reception. When a manufacturing process started in a SYNSET firm, a notification was sent to the Semantic Enterprise Context Monitor that there is not enough available raw materials from supplier to continue displaying such a customer order. In this case, ERP Inference Engine should evaluate another available semantic equivalent services paths but with different QoS in order to replacing supplier service at run-time. We are used [START_REF] Alti | USSAP: Universal Smart Social Adaptation Platform[END_REF] for classifying the relevant cloud services that have potential benefit. Table .3 shows the evaluation results, meaning that services orchestration "RawMateriel_eMail_SuplierLocal + PaymentOrder_Local" is selected as the best composition path. This selection based on the high score compared to other paths.

CONCLUSION

In this article, we have proposed a new hybrid approach based on semantic web technologies and cloud computing. We propose also CloudCxQERP platform, which is cloud-based semantic platform for dynamic management of context-aware mobile ERP applications. The main objective of this platform is the selection of relevant services and adaptation of ERP applications more quickly and cost effectively. In future works, we intend to refine costumers' requirements. Moreover, we will develop heuristic parallel genetic method at design and at run-time phases.

Figure 1 .

 1 Figure 1. CxQCloudERP Ontology structure.Enterprise has a name, unique location, functional and non-functional requirements.Business processes describes various business processes such as sales, purchasing, opportunity, order, etc.  Cloud Service has a name, a version and unique ID (URI).  QoS which describes the quality of service parameters such as efficiency, security, availability, etc.  Entity which describes different types of resources: staff, tasks, equipments, machines, raw materials, etc.Context Entity which defines all information that can be collected from the company (needs and preferences), process (deployment requirements and execution) and resource (resource type and constraints).  Constraint which describes a set of OWL logical expressions of various mobile agents and staff.Cloud provider that provides business services  Cloud Platform Context-aware-Service is used for the deployment of several business services.

Figure 2 .

 2 Figure 2. Enterprise sun-ontology.

Figure 3 .

 3 Figure 3. Entity sub-ontology.

Figure 4 .

 4 Figure 4. Context-Entity sub-ontology.

Figure 5 .

 5 Figure 5. Business process sub-ontology.

Fig. 5

 5 shows following concepts:Service has a name, a version and unique ID (URI).  Service_Location: the location which the service facilities are located.

Figure 6 .

 6 Figure 6. Context sub-ontology (a) Cloud provider sub-ontology (b) -Cloud service sub ontology

Figure 7 .

 7 Figure 7. Virtual cloud service ontology.



  ERP Cloud Proxy: it stores or serves as an ERP Cloud proxy for cloud business services and ERP resources. It also executes cloud business services. This component relies on the following sub-components: (1) -the Semantic Customer Context Monitor is responsible of verifying user's context change by contacting the Cloud Context Management component, (2)the Semantic Entreprise Context Monitor is responsible of verifying enterprise resources and context changes and (3) -the ERP Inference Engine supports the building of personalized business process from available services.

Figure 8 .

 8 Figure 8. The general architecture of CloudCxQERP platform

Table 1 . Contextual constraints specified by a customer Location Quality and Contextual constraints

 1

	Any	Shortest delivery time and high-quality packaging boxes
	Near a Company	Receives 10% discount based on a large quantity of packaging boxes without extra travel fees and notification by email or phone call
	Car	Notification only by phone call and prefers high-security transaction

Table 2 . Clustering contextual constraints of both companies Company Quality and contextual preferences

 2

	Both	City= SETIF AND Type=Deposit
	SYNSET	Availability=High AND Supplier=Local AND Price=Low
	AGROFILM	TransactionType=Confidential AND Supplier =Any AND Payment=Fast

Customer Mobile application CloudCxQERP DW Figure 9. An abstract collaborative business process.

Table 3 . Evaluation results

 3

	Services composition paths	Score
	SaaS_Service_RawMateriel_eMail_SuplierLocal >> SaaS_Service_PaymentOrder_Local	0.79 ms/$
	SaaS_Service_RawMaterielSuplier_WebSite >> SaaS_Service_InputCustomerCreditCard	0.65 ms/$


SAFEST (Severe Accident Facilities for European Safety

Targets) is a 48 month EU-supported pan-European Integrated Research Infrastructure Initiative for increased safety of nuclear systems at EU level with 8 partners from 6 European countries. Zr Hf Ce Electronic structure 5f 3 -6d 1 -7s 2 7s 2 -6d 1 4d 2 -5s 2 4f 14 -5d 2 -6s 2 4f 1 -5d1-6s 2

Oxidation state 0,+3,+4, +5, + 6 0, +3 0,+1,+4 0,+4 0,+1,+3,+4 Formerly UO 2 Presently Al 2 O 3 Heated length of the test bundle is 600 mm.

SAFEST FACILITIES-KROTOS (CEA)

QUENCH LIVE DISCO MOCKA The KROTOS facility is dedicated to fuel coolant interaction (jet fragmentation, debris formation, steam explosion). ~5 kg of corium at more than 2850 °C are dropped in water. The facility consists of four main parts:

The furnace (1) is a water cooled stainless steel container designed to withstand 4 MPa pressure and is equipped with a three-phase cylindrical heating resistor. The transfer channel (3) is a vertical tube, connecting the furnace and the test section. After corium transfer, the valve (2) is closed. The test section consists of a pressure vessel (4) with a test tube inside (5). The test tube is a free standing cylinder filled with water. The X-Ray radioscopy system can trace the fragmentation of the melt.

SAFEST FACILITIES-FLF (JRC)

QUENCH LIVE DISCO MOCKA The FLF test facility is a laser heating/melting and fast pyrometry facility (to measure the temperature) to study the melting behaviour of pure compounds and binary systems. Experiments are carried out in an autoclave under medium to high pressure (from a few tenths up to a few hundreds MPa) of an inert gas (helium or argon) in order to suppress as much as possible evaporation phenomena in highly volatile samples and to study the behaviour of phase transition points as a function of pressure.

SAFEST FACILITIES-COMETA (UJV)

QUENCH LIVE DISCO MOCKA The cold crucible induction furnace test facility COMETA is constructed for powder oxide materials melting with melt temperatures up to 3000 °C. The facility is adopted for the operation with radioactive materials (in particular with UO 2 ) and intended for simulating real corium melts. The capacity of the cold crucible is about 1 kg of initial corium batch (250 cm 3 volume of melt). The power supply is a high-frequency generator with max. 80 kW at 4.5 MHz frequency.

  Development of research roadmaps to focus future European R&D on the stabilisation and termination of severe accidents in PWRs and BWRs Establishing the access to SAFEST research infrastructure to investigate all important phenomena from the early core degradation to corium pool formation in the lower head, and ex-vessel melt situations Creation of an integrated pan-European laboratory for severe accident research able to address and successfully resolve the wide variety of issues related to severe accident analysis and corium behaviour Continuous improvement and upgrading of the SAFEST infrastructure to increase the experimental capabilities and overall quality of R&D to meet current and future challenges Applications of the results of the project to the European light water reactors SAFEST PROJECT MAIN GOALS NUMAT | Montpellier | November 2016 | PAGE 5 Element U Al

  morphology, show that the debris resulting from the alumina pours is significantly different from the finely fragmented corium debris with a Mass Mean Diameter of about 1.7 mm (e.g. KROTOS 45) compared to larger alumina debris (e.g. 17 mm MMD in KROTOS 41); The more coherent corium jet pour tends to penetrate deeper into the water pool than an alumina jets; Different droplet solidification patterns are expected between opaque corium droplets and semi-transparent alumina particles(Dombrowski & Dinh, NED 238:1421-1429, 2008) while «the mitigating effect of solidification appeared quite clearly in[…] experiments»(Meignien et al., Ann. Nucl. Energ. 74 (2014) 125-133) The oxidation of UO 2 by steam produces incondensable gases that may blanket the droplets and prevent explosions, but these exothermal oxidation reactions may also increase the explosion yield…. NUMAT | Montpellier | November 2016 KROTOS experiments, Huhtiniemi & Magallon, Nucl. Eng. Des., 2001 | PAGE 10 Solidification of fine debris with alumina ? Metastable low-melting point g-Al 2 O 3 Post test XRD of alumina samples (from KROTOS and PREMIX facilities) have shown that, in case of vapour explosion, a significant part of the smaller debris is g-Al2O3. g-Al2O3 is a metastable phase Solidifies several 100s K below solidification point of a-Al2O3 (2050°C) No similar metastable phase observed in (U,Zr)O 2 system Role of subcooling has been reported by numerous authors for FCI Difficult to reproduce/scale from one material to the other… Tyrpekl & Piluso, Annals Nucl. Energ. 46 (2012) 197-203 | PAGE 11 WHY PROTOTYPIC CORIUM ? Differences between corium and alumina in FCI tests Zirconia also behaved differently from corium in some TROI FCI tests Similar issues clearly arises for experiments where mass between oxide and metal phases is important: crust formation and solidification stratification both in-vessel and ex-vessel evolution of metallic layer during Molten Core Concrete Interaction Impossible simulation of phenomena which are mainly controlled by corium crust strength, such as underwater spreadingIn the absence of reliable thermophysical properties for corium, it is anyhow difficult to find a satisfactory scaling.Solutions (in complement to simulant material experiments):In pile experiments (e.g. Phebus FP)Experiments with irradiated fuel (e.g. VERCORS/VERDON) facility investigates the early phase severe accident phenomena with small electrically heated bundles. 7-rod VVER geometry and 9-rod PWR bundle geometries were used for experiments. CODEX-COOL: coolability tests with 19 rods. Pellets are applied for the simulation of LOCA scenarios (up to 1200 °C).

  rotating plasma arc furnace able to melt ~80 kg of corium at temperatures of up to 3000 °C (in-or ex-vessel corium) and to pour the melt in a dedicated test section. Corium can be poured into a crucible in which the decay heat is simulated by a 150 kW inductor device.Experiments to studyCorium spreading over different types of substrates (ceramic, concrete…) Corium solidification Corium pool thermalhydraulics Corium progression in debris bed Long term interaction of corium with materials such as concrete or materials to be used in a core catcher Validation of corium cooling strategies Simultaneous Interaction of Molten Corium with Protective and Sacrificial Material) test facility studies different phenomena of the interaction of molten corium with concrete or other sacrificial material and with protective material and to generate data on thermophysical and thermochemical processes during interaction of melt with concrete or other material. Centerpiece of SICOPS is a high-frequency (1 -4 MHz) furnace with a cold crucible (diameter of up to 20 cm). Tests with material masses of up to 20 kg molten material (simulant or prototypic, with temperatures up to 3000 °C) and with a generator power of max. 100 kW can be performed. et al., An Experimental Study of the Effect of Boron Carbide on the SFR Corium Composition, IYNC 2010, Capetown.

  Possible crucible shift: 0-50 mm/min Melt sampling, aerosol and melt composition analysis New larger facility under commissionning within SUSEN PLINIUS-2 CEA future platform for prototypic corium experimental R&D (commissioning ~2021) For ASTRID SFR severe accidents For LWR severe accidents Will be the successor of current PLINIUS platform (VULCANO, VITI, KROTOS). One Furnace (Cold crucible induction -~500 kg) 3 experimental halls Corium/sodium Ablation-Mitigation Corium-Water Smaller scale / simulant material facilities. NUMAT | Montpellier | November 2016 | PAGE 20

UO 3 vaporize at relatively low temperature -Their release modify oxygen stoichiometry of the liquid phase.

	Effective Ionic radius r	3+, r= 102.5				3+, r=101
	(pm)	4+, r = 89	3+, r=53.5	+4, r=72	+4, r=71	4+, r= 87
		5+, r= 76
		6+, r= 73
						4 O 9 , U 3 O 7 , U 3 O 8 .
	Liquid state: Metallic liquid: U with dissolved oxygen; oxidic liquid: UO 2±x
	Gaseous state: U, UO, UO 2 , and UO 3
	-UO and

Uranium has 4 oxidation states (all of which may exist during severe accident scenarios) leading to many species in the U-O system: Solid state: U, UO 2 (actually may vary from UO 1.70 to UO 2.25 ), U NUMAT | Montpellier | November

2016 Oxides Melting point (K) Density (293K) (g.cm -3 ) Specific heat (J.K -1 .kg -1 ) Latent heat fusion (kJ.kg -1 ) UO 2

		Tm = 3120  30	10.956	~300	260
	CeO 2	3073	7.10	~300	465
	HfO 2	3031  25	9.68 (monocl.)	~300	457
	ZrO 2	2973	5.68 (monocl.)	~300	810
	Al 2 O 3	2072	3.95	~700	1140
					| PAGE 9

This work has been released into the public domain by its

CHALLENGES OF PROTOTYPIC CORIUM EXPERIMENTAL RESEARCH

High temperatures (1500-3000 K) depending to the materials which may have mixed with molten fuel

In vessel (U, Zr)O 2 melts ~3000 K Ex vessel corium melts with large fraction of concretes  ~2000 K There is not ONE corium but a continuum of compositions depending on: Scenarios Time-evolution within a given scenario Physical properties are not well known and are evolving during experiment and from one test to another.


B Destouches

L Geslot

M Vermeeren

Schyns

Abstract-In order to ensure the quality and the relevance of irradiation programs in the future Jules Horowitz Reactor (JHR), the French Alternative Energies and Atomic Energy Commission (CEA) has significantly increased its research and development effort in the field of in-pile instrumentation during the last decade. Major progresses have thus been achieved in the capability to perform accurate in-pile measurements using reliable and updated techniques.

A significant part of this effort have been conducted in the framework of the Joint Instrumentation Laboratory between the CEA and the Belgian Nuclear Research Centre (SCK•CEN).

In order to improve measurement techniques for neutron flux assessment, a unique system for online measurement of fast neutron flux has been developed and recently qualified in-pile. The Fast-Neutron-Detection-System (FNDS) has been designed to monitor accurately high-energy neutrons flux (E > 1 MeV) in typical Material Testing Reactor conditions, where overall neutron flux level can be as high as 10 15 n.cm -2 .s -1 and is generally dominated by thermal neutrons. Moreover, the neutron flux is coupled with a high gamma flux of typically a few 10 15 .cm -2 .s -1 , which can be highly disturbing for the online measurement of neutron fluxes.

The patented FNDS system is based on two detectors allowing the simultaneous detection of both thermal and fast neutron flux. Thermal neutrons can be measured using a Self-Powered-Neutron-Detector (SPND) or a 235 U miniature fission chamber, while fast neutron detection requires a miniature fission chamber with a special fissile material presenting an energy threshold near 1 MeV, which can be 242 Pu for MTR conditions. Fission chambers are operated in Campbelling mode for an efficient gamma rejection. FNDS also includes a specific software that processes measurements to compensate online the fissile material depletion and to adjust the sensitivity of the detectors, in order to produce a precise evaluation of both thermal and fast neutron flux even after long term irradiation. This paper describes the innovative features of FNDS and discusses the results of its final in-pile qualification. FNDS is now operational and is assumed to be the first and unique acquisition system able to provide an online measurement of the fast neutron flux in MTR conditions. This system will of course be used to perform spectral neutron characterization of JHR channels, but it may also be implemented in future irradiation experiments, for a better and real-time evaluation of the fast neutron flux received by material and fuel samples.

FNDS has been validated through a two-step experimental program. A first set of tests was performed at BR2 reactor operated by SCK•CEN in

Index Terms-Nuclear Measurements, Fission Chamber, Reactor Instrumentation

I. INTRODUCTION

AST neutron flux (E ≥ 1 MeV) is a key neutron parameter in nuclear reactors. Its measurement is particularly relevant to assess material damage under irradiation. In very low power research reactors, such as ZPRs (Zero Power Reactors), the fast neutron flux is usually measured on-line with specific fission chambers, containing for example 238 U coating. In more powerful reactors, such as Material Testing Reactors (MTRs) or nuclear power plants (NPPs), techniques used in ZPRs are not appropriate. This is mainly due to the rapid evolution of the fissile coating under high neutron flux that reduces the sensitivity of the sensor to the 'fast' spectrum domain (cf. II.B). This neutron parameter is therefore not accessible on-line and is generally evaluated afterward by activation dosimetry or by Monte Carlo calculations. However, in the current context of increasing requirements for in-pile measurements, particularly for experiments in MTRs, the on-line measurement of fast neutron flux has become a major objective.

In order to ensure the quality and the relevance of irradiation programs in the future Jules Horowitz Reactor (JHR), the French Alternative Energies and Atomic Energy Commission (CEA) has significantly increased its research and development effort in the field of in-pile instrumentation during the last decade. In this context and in the framework of the Joint Instrumentation Laboratory between the CEA and the Belgian Nuclear Research Centre (SCK•CEN), a measurement system dedicated to on-line measurement of fast neutron flux has been developed.

Next paragraphs describe the innovative features of the Fast-Neutron-Detection-System (FNDS) and discuss the results of its final in-pile qualification. F because of the low neutron sensitivity of this type of fission chamber, the high gamma flux of MTRs leads to a high gamma contribution to the signal. Furthermore, neutron captures on the nuclei of the fissile coating create isotopes that have a high cross-section in the thermal spectrum domain.

Thus the 'fast' sensitivity of the detector decreases rapidly. Isotopic impurities of the coating must also be taken into account. Figure 1 illustrates this difficulty for the use of 242 Pu, which has a suitable response in the fast spectrum domain [START_REF] Filliatre | Reasons why Plutonium 242 is the best fission chamber deposit to monitor the fast component of a high neutron flux[END_REF]. We can notice that impurities and isotopes created by neutron captures induce an increasingly high sensitivity to thermal neutrons.

A. FNDS general description

In order to compensate difficulties mentioned above, FNDS includes an acquisition system allowing measurements with fission chambers in Campbelling mode. This mode allows a very efficient rejection of the gamma contribution [START_REF] Vermeeren | Experimental Verification of the Fission Chamber Gamma Signal Suppression by the Campbelling Mode[END_REF] while offering a very wide dynamic range. FNDS also integrates signal processing software that firstly calculate in real time the evolution of the fissile coating of the fission chamber using an evolution code DARWIN [3], and that secondly deduce the corresponding evolution of the neutron sensitivity of the fast fission chamber. This software extracts in real-time the value of the fast neutron flux from the signal.

In order to allow a precise evaluation of thermal neutron fluence received by the 'fast' fission chamber, FNDS is equipped with a second detector that is sensitive to thermal neutron flux and positioned near the 'fast' fission chamber. This second detector may be a fission chamber with 235 U coating or a Self-Powered Neutron Detector (SPND). FNDS thus consists in 2 detectors for 'fast' and 'thermal' spectrum domains, an acquisition system operating in Campbelling mode and a calculation module for on-line analysis of neutron fluxes (Figure 2). These innovations were patented for the detectors [START_REF] Oriol | Détecteur pour la mesure en ligne des neutrons rapides dans un réacteur[END_REF] and for the entire measurement system [START_REF] Oriol | Dispositif de mesure en ligne d'un flux de neutrons rapides et épithermiques[END_REF].

B. Detectors

An important bibliography study was carried out to establish the state of the art of neutron and photonic measurements in MTRs [START_REF] Fourmentel | Etat de l'art des mesures neutroniques et photoniques en réacteur d'irradiation pour la cartographie du coeur du réacteur Jules Horowitz[END_REF][START_REF] Fourmentel | Mesures neutroniques et photoniques combinées pour la caractérisation précise des canaux expérimentaux du futur réacteur d'irradiation Jules Horowitz (RJH)[END_REF], and led us to select fission chamber sensors. It was shown that the most suitable fissile isotope for the estimation of the fast neutron flux is 242 Pu under the typical irradiation conditions of MTRs, such as OSIRIS (CEA Saclay -France), BR2 (Mol -Belgium) and JHR reactors [START_REF] Filliatre | Reasons why Plutonium 242 is the best fission chamber deposit to monitor the fast component of a high neutron flux[END_REF][START_REF] Filliatre | Joint estimation of the fast and thermal components of a high neutron flux with a two on-line detector system[END_REF]. Figure 3 illustrates the raison why 242 Pu coating is the best choice compared to other fissile isotopes; it shows the stability of its fast neutron sensitivity at high fluence. A typical 'fast' fission chamber selected for MTRs contains 100 μg of 242 Pu while the typical associated 'thermal' detector contains 10 μg of 235 U. Both sensors have a 3 mm outer diameter body and integrated mineral insulated cable.

These sensors have been developed and manufactured at the Fission Chamber Manufacturing Workshop located in CEA Cadarache.

As explained previously, the analysis of fission chamber signal requires a precise knowledge of its isotopic composition and as well as the effective deposited mass of fissile materials.

It is necessary to quantify with the best accuracy their contribution to the signal. For the in-pile qualification of FNDS ( §.III), analyzes of fissile coatings give the following results:

As a reminder, the odd isotopes of plutonium are mostly fissile in the 'thermal' spectrum domain and have very high fission cross sections compare to 242 Pu in the 'fast' spectrum domain (cf. Figure 1).

C. Acquisition system

The acquisition system has 2 channels for the sensor couple. Each channel is equipped with a high-voltage power supply for polarization with adjustable voltage (typically 250 V to reach the saturation plateau of a 3 mm fission chamber). The measured low-level current signals pass through a preamplifier (manufactured by CEA Saclay), amplification stage and then are filtered and digitalized for processing with the measurement interpretation software.

There are typically 3 modes to operate fission chambers: pulse, fluctuation (or Campbelling) and current modes. In MTRs conditions, the advantage of the Campbelling mode is the radical limitation of the gamma contribution on the signal [START_REF] Vermeeren | Experimental Verification of the Fission Chamber Gamma Signal Suppression by the Campbelling Mode[END_REF] compared to current mode. Indeed, MTR neutron fluxes are too high to operate such fission chambers in pulse mode.

In Campbelling mode, the measurement of the variance of low fission chamber signal with a specific bandwidth has required the development of a dedicated acquisition system. As such a system was not available from Industry; prototypes were specially designed at CEA. Studies are on-going for the industrialization of such acquisition system in a short term [START_REF] Thevenin | Digital Real-Time Multiple Channel Multiple Mode Neutron Flux Estimation on FPGA-based Device[END_REF].

D. Processing software

The FNDS processing software calculates the sensitivity of each fission chamber to the thermal, epithermal and fast neutrons from specific input data and then calculates the thermal and fast neutron fluxes versus time. Input data required for the processing are the following:

-typical neutron spectrum at the location of the detectors, -nuclear data library, -mass and isotopic composition of the fissile coatings, -calibration coefficient of the fission chambers ( §.III.B), -calibration coefficient of the electronic ( §.III.B), -variance measurements.

Processing software includes DARWIN code [START_REF] Fourmentel | Etat de l'art des mesures neutroniques et photoniques en réacteur d'irradiation pour la cartographie du coeur du réacteur Jules Horowitz[END_REF], developed by the CEA, to compute fission cross sections of each isotope present in the fissile coating and to calculate the isotopic evolution of these coatings with fluence and time. The method to combine measurements and estimate fast neutron flux is the following [START_REF] Fourmentel | Mesures neutroniques et photoniques combinées pour la caractérisation précise des canaux expérimentaux du futur réacteur d'irradiation Jules Horowitz (RJH)[END_REF][START_REF] Filliatre | Joint estimation of the fast and thermal components of a high neutron flux with a two on-line detector system[END_REF]:

If R1 is the fission rate of the 'fast' fission chamber and R2 the fission rate of the 'thermal' fission chamber, the following two equations can be written:

𝑅 1 = 𝑆 𝑡ℎ 1 ⋅ Φ 𝑡ℎ + 𝑆 é𝑝𝑖 1 ⋅ Φ é𝑝𝑖 + 𝑆 𝑟𝑎𝑝 1 ⋅ Φ 𝑟𝑎𝑝 𝑅 2 = 𝑆 𝑡ℎ 2 ⋅ Φ 𝑡ℎ + 𝑆 é𝑝𝑖 2 ⋅ Φ é𝑝𝑖 + 𝑆 𝑟𝑎𝑝 2 ⋅ Φ 𝑟𝑎𝑝
with Si thermal, epithermal and fast neutron sensitivity [1/n.cm -2 ] and j thermal, epithermal and fast neutron fluxes [n.cm -2 .s -1 ].

To solve this system of 2 equations with 3 unknown variables we assume that the ratio between epithermal and fast fluxes is constant during irradiation:

Φ é𝑝𝑖 = 𝛼 ⋅ Φ 𝑟𝑎𝑝
This hypothesis was verified by calculations [START_REF] Geslot | Qualification du système d'acquisition FNDS lors de l'expérience FICTIONS-8[END_REF]. It allows to reduce our system to 2 equations with 2 unknown variables and to establish the following equation of the fast neutron flux:

Φ 𝑟𝑎𝑝 = 𝑅 1 𝑆 𝑡ℎ 1 - 𝑅 2 𝑆 𝑡ℎ 2 𝛼 ⋅ 𝑆 é𝑝𝑖 1 + 𝑆 𝑟𝑎𝑝 1 𝑆 𝑡ℎ 1 - 𝛼 ⋅ 𝑆 é𝑝𝑖 2 + 𝑆 𝑟𝑎𝑝 2 𝑆 𝑡ℎ 2
However, in a light water reactor, 235 U coating is predominantly (~99% initially) sensitive to thermal neutrons [START_REF] Fourmentel | Etat de l'art des mesures neutroniques et photoniques en réacteur d'irradiation pour la cartographie du coeur du réacteur Jules Horowitz[END_REF][START_REF] Fourmentel | Mesures neutroniques et photoniques combinées pour la caractérisation précise des canaux expérimentaux du futur réacteur d'irradiation Jules Horowitz (RJH)[END_REF] and makes it possible to measure thermal neutron flux without calculation assumptions. In this case, fast neutron flux can then be written in the following reduced form:

Φ 𝑟𝑎𝑝 = 𝑅 1 -𝑆 𝑡ℎ 1 ⋅ Φ 𝑡ℎ 𝑆 𝑟𝑎𝑝 1 + 𝛼 ⋅ 𝑆 é𝑝𝑖 1

III. IN-PILE QUALIFICATION OF FNDS

FNDS has been validated through a two-step experimental program. A first set of tests was performed at BR2 reactor through the FICTIONS-8 experiment. Two FNDS prototypes were operated in-pile during nearly 1000 hours. This experiment exhibited the consistency of the measurement of thermal to fast neutron flux ratio with Monte Carlo calculations, as well as the right compensation of fissile material depletion [START_REF] Geslot | New measurement system for on line in core highenergy neutron flux monitoring in materials testing reactor conditions[END_REF].

A second set of tests was then necessary to qualify the absolute fast neutron flux measurement process. The main requirement was to perform measurements in well-controlled conditions (neutron spectrum, flux gradient, etc.) in order to minimize external sources of uncertainty and bias. In this context, it was decided to carry out the EFICAF (Estimation of Flux in Isis by Fission Chamber) experiment in ISIS reactor (CEA Saclay, France). ISIS reactor allows stable and reproducible irradiation conditions. Its neutron flux level is also sufficient to operate fission chambers in Campbelling mode. Moreover, its flexibility allowed to carry out all the measurements that were necessary for the qualification (online measurements and activation dosimetry measurements without external disturbance).

A. EFICAF specifications

It was decided to measure the absolute thermal and fast neutron fluxes in a single irradiation channel of ISIS core. Fission chambers were located on both sides of the maximum neutron flux height in order to receive the same neutron flux with a simple experimental device and to avoid a moving device. Reactor power was specified at 250 kWth, on one hand for easy implementation of the experiment and on the other hand to minimize the fluence and the corresponding evolution of fissile materials.

For this experiment, reference neutron flux measurements were performed using activation dosimeters. They provided a precise evaluation of the neutron flux profile in the experimental channel. These dosimeters were the following:

-7 pure iron (diameter 10 mm, thickness 100 µm), -7 pure nickel (diameter 10 mm, thickness 125 µm), -7 cobalt (AlCo1% -diameter 10 mm, thickness 100 µm) including 1 in a cadmium shield (for the epithermal ratio on cobalt).

Finally, Monte Carlo calculations were performed with TRIPOLI4 code [START_REF] Brun | TRIPOLI-4®, CEA, EDF and AREVA reference Monte Carlo code[END_REF] for FNDS processing software and for activation dosimeter analysis.

B. Fission chambers calibration in Campbelling mode

A method to calibrate fission chambers in Campbelling mode has been developed [START_REF] Geslot | Method to calibrate fission chambers in Campbelling mode[END_REF]. This calibration was carried out for FNDS sensors in MINERVE zero power reactor at CEA Cadarache.

In this calibration it is assumed that the measured signal is equal to the product of the coating fission rate, a calibration coefficient of the electronic and a calibration coefficient of the detector. The variance of the signal can then be expressed according to the following relation:

𝜎 𝑉 2 = 𝐾 𝑒 • 𝐾 𝑑 • 𝑅 with: 𝜎 𝑉 2 : variance measurement [V 2 ]
, 𝐾 𝑒 : coefficient of the electronic channel [Ω 2 .Hz], 𝐾 𝑑 : calibration coefficient of the detector [A 2 .Hz -2 ] and 𝑅: total fission rate of the coating [Hz]. This method was used for the first time in 2012 for the CARMEN-1 experiment led in OSIRIS reactor at CEA Saclay. It exhibited excellent results for 235 U fission chamber used in Campbelling mode by comparison with rhodium SPND and activation dosimeters [START_REF] Fourmentel | Comparison of Thermal Neutron Flux Measured by 235U Fission Chamber and Rhodium Self-Powered Neutron Detector in MTR[END_REF]. EFICAF experiment was therefore the second opportunity to use this calibration method. The calibration coefficient obtained for both fission chambers is:

K d = 3.75. 10 -27 A 2 . Hz -2
We can notice that the calibration coefficient is the same for both fission chambers. Indeed, the average charge of a fission product between the electrodes is mostly similar for both coatings. This calibration coefficient value also depends on the signal bandwidth. It is therefore important to characterize the bandwidth of the analogue-to-digital processing for an appropriate use of the calibration coefficient. Details about the calibration are given in [START_REF] Geslot | Method to calibrate fission chambers in Campbelling mode[END_REF][START_REF] Fourmentel | Méthode et protocole d'étalonnage des chambres à fission en mode fluctuation[END_REF].

According to the relation between fission rate 'R' and variance measurements 𝜎 𝑉 2 there is a second calibration coefficient 𝐾 𝑒 to be calculated. 𝐾 𝑒 links the current signal delivered by the fission chambers to the corresponding variance [V 2 ] for each measurement channel.

The calibration coefficients of the electronics for the 2 measurement channels of FNDS were the following: The results of TRIPOLI4 calculations and dosimetry measurements are in good agreement to assess neutron fluxes values given by FNDS.

K 𝑒(𝑐ℎ𝑎𝑛𝑛𝑒𝑙1) =
Thermal and fast neutron fluxes measured by FNDS are compared with values given the TRIPOLI4 code and activation dosimetry respectively in Table 2 and Table 3. These results confirm the good agreement for absolute thermal neutron flux using Campbelling mode operation and the corresponding calibration process [START_REF] Fourmentel | Comparison of Thermal Neutron Flux Measured by 235U Fission Chamber and Rhodium Self-Powered Neutron Detector in MTR[END_REF].

TABLE III FAST NEUTRON FLUX EVALUATIONS AND DEVIATIONS BETWEEN FNDS AND TRIPOLI4 CALCULATIONS AND BETWEEN FNDS AND FE, NI MEASUREMENTS

The deviation between FNDS fast neutron flux measurements and dosimetry results is mainly covered by the uncertainties of the activation dosimetry measurements (typically 4% for fast neutron flux evaluation). The results of this experiment confirm the quality of the on-line fast neutron flux measurements at a few 10 11 n.cm -2 .s -1 (E > 1 MeV) with this type of miniature fission chambers and therefore confirm FNDS capability to measure in real-time the absolute thermal and fast neutron fluxes with an excellent accuracy.

IV. CONCLUSION EFICAF experiment demonstrated the ability of FNDS to measure on-line thermal and fast neutron fluxes. Deviations between fast neutron flux measured by FNDS and activation dosimetry results are below 5%. These results confirm the excellent accuracy of the analysis method using a couple of 235 U and 242 Pu fissile coatings and special calibration in Campbelling mode.

FNDS is now operational and is assumed to be the first and unique acquisition system able to provide an on-line measurement of the fast neutron flux in MTR conditions. This system will of course be used to perform spectral neutron characterization of JHR channels, but it may also be implemented in future irradiation experiments, for a better and real-time evaluation of the fast neutron flux received by material and fuel samples. Further studies are on-going to update the FNDS acquisition system using a multi-channel and multi-mode acquisition system called MONACO (Multichannel Online Neutron Acquisition in Campbell mOde) [START_REF] Thevenin | Digital Real-Time Multiple Channel Multiple Mode Neutron Flux Estimation on FPGA-based Device[END_REF][START_REF] Barbot | On Line Neutron Flux Mapping in Fuel Coolant Channels of a Research Reactor[END_REF]. Studies are also carried out to assess the uncertainty associated with neutron flux analysis calculations, mainly for the calibration coefficients of the detectors. Finally, additional tests will shortly be performed to evaluate FNDS ability to measure also the epithermal neutron flux.

  Belgium. Two FNDS prototypes were operated in-pile during nearly 1000 hours. These tests exhibited the consistency of the measurement of thermal to fast neutron flux ratio with Monte Carlo calculations, as well as the right compensation of fissile material depletion. Then a second test was recently completed at ISIS reactor operated by CEA in France. For this irradiation, FNDS signal was compared to reference thermal and fast neutron flux measurements using activation dosimeters analyzed under COFRAC® Quality Certification. During this latter test, FNDS proved its ability to measure online the fast neutron flux with an overall accuracy better than 5%.

  II. FNDS SPECIFICATIONS On-line measurements of fast neutron flux are based on the use of a fission chamber with a specific fissile coating, presenting an energy threshold near the fast domain (E > 1 MeV). If the use of such fission chamber is not a problem at very low neutron flux, it faces major difficulties under intense mixed neutron and gamma fluxes. First of all, In-Pile Qualification of the Fast-Neutron-Detection-System D. Fourmentel 1 , J-F. Villard 1 , C. Destouches 1 , L. Barbot 1 , B. Geslot 2 , L. Vermeeren 3 , M. Schyns 3

Fig. 1 .

 1 Fig. 1. Fission cross sections of a typical 242 Pu fissile coating showing that impurities and isotopes created by capture must be taken into account for fast neutron flux measurements

Fig. 2 .

 2 Fig. 2. Overview of FNDS components

Fig. 3 .

 3 Fig.3. Sensitivity to fast neutrons for initially pure deposits under typical BR2 neutron spectrum[3]

Fig. 4 .

 4 Fig. 4. Illustration of updated FNDS based on the Multichannel Acquisition System MONACO [9].

  5.45. 10 15 Ω 2 . Hz K 𝑒(𝑐ℎ𝑎𝑛𝑛𝑒𝑙2) = 4.91. 10 15 Ω 2 . HzC. Monte Carlo and experimental resultsActivity measurements of the EFICAF dosimeters were carried out at the MADERE platform of CEA Cadarache under COFRAC ® Quality Accreditation[16]. Table1below summarizes the neutron flux results calculated by TRIPOLI4 code and measured by activation dosimetry at the locations of the two fission chambers.


G Mossand

D Guillaumont

M Miguirditchian

Daniel Imbert

M Mazzanti

E Andreiadis
email: eugen.andreiadis@cea.fr

Optimization of novel bifunctional ligand design for uranium extraction

Keywords: Liquid-liquid extraction, organic synthesis, bifunctional ligands, uranium

Abstract

Uranium is recovered from natural primary and secondary resources by hydrometallurgical processes including ore leaching and purification. Different processes are available for uranium extraction and separation from the associated elements, but the most widely used remains the liquid-liquid extraction. Improving the performance of current processes through the design of more efficient, selective and robust extractants is particularly important.

To optimize the liquid-liquid extraction of uranyl (UO2 2+ ) from highly complexing sulfuric or phosphoric acid solutions, an approach based on the design of bifunctional extractants combining in the same molecular architecture both cation exchanger and neutral-donor functionalities has led to the development of a new family of ligand molecules containing a pyridine N-oxide ring and an acidic phosphonate function.

Several novel molecules have been synthesized using an optimized strategy. The affinity and selectivity of the extractants were evaluated by batch liquid-liquid extraction tests and very high distribution ratios of uranium(VI) were measured (DU > 4200) in the presence of large concentrations of complexing anions such as sulfates. However, the separation factor of U(VI) versus Fe(III) is low.

To establish structure-activity correlations and understand the lack of selectivity of this family towards Fe(III), the molecular environment around U(VI) and Fe(III) cations has been investigated using a combination of experimental (UV-vis and FT-IR spectroscopy, ESI-MS spectrometry) and theoretical approaches. Density functional theory (DFT) calculations coupled to infrared spectroscopy have been


P Martin

D Prieur

R Bes

R Belin

M Strach

D Manara

C Valot

T Vitova

T Prussman

K Dardenne

Study of the 5f electronic states in u, np, pu and am and (u,pu) oxides using high resolution xanes

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Because of their specific neutronic spectrum, SFRs will also be able to burn long-lived minor actinides (MAs) such as Am and Np. One of the considered options is to add them homogeneously to the fuel in significant amounts (2-6%). In such complex mixed oxide systems, both the homogeneity of the cation distribution into the fluorite UO 2 structure and the oxygen stoichiometry significantly affects most of the fuel properties (thermal conductivity, melting temperature, diffusion phenomena, etc.). These properties depend mainly on the complexity of actinide electronic structures, in particular the unfilled actinide 5f valence shells. The latter can be directly probe with High Resolution X-ray spectroscopy performed at the M 4,5 (3d→5f transition) edges of actinides. Here, we show results obtained on binary oxides (UO 2 , NpO 2 , PuO 2 and AmO 2 ) and U 1-y Pu y O 2 (y=0.24, 0.44 and 0.62) samples combined with theoretical calculations with FDMNES [1] code.

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Conclusions

The gain in selectivity and resolution provided by HERFD at M 4,5 (3d →5f) edges of actinides (U-Am) allow to precisely probe the 5f (and 6d) occupancy experimentally. Charge transfer between An valence shells (5f -6d) and O 2p must be considered in FDMNES calculations to reproduce experiment. PuO 2 and AmO 2 (with 6d 0 in the ground state) experimental spectra are well reproduced with the following 5f configurations: Pu = 5f 4.4 and Am = 5f 5.6 . For UO2 and NpO2 (with 6d1 in the ground state), 6d and 5f have to be taken into account in the charge transfer with O 2p but results obtained with 6d 0 and 6d 1 with different 5f occupancies are not satisfactory. Calculations with an intermediate 6d occupancy are in progress. Contrary to the XANES measurements, the substitution of U by Pu atoms is observed at both U and Pu edges in HERFD-XANES spectra collected on (U,Pu)O 2.00 samples [6]. First calculations show that charge transfer has to be introduced in both U (5f-6d) and Pu (5f ).

Results on binary systems

Measurements on AnO 2

Sample holder

Results considering 6d 1 0 20 40 60

Np-M 4 normalized µ (A.U.)

Energy-E 0 (eV) 5f 2.3 6d 1 7s 2 5f 2.2 6d 1 7s 2 5f 2.1 6d 1 7s 2 5f 2.0 6d 1 7s 2 NpO 2

Results on

  , DEN/DTEC/SECA/LCC, Bagnol sur Cèze, France; 2-European Commission, JRC-Karlsruhe, Germany; 3-Antimatter and Nuclear engineering, Department of applied Physics, Aalto University, Finland; 4-CEA, DEN/DEC/SESC/LLCC, Cadarache, France; 5-Karlsruhe Institute of Technology, Institute for Nuclear Waste Disposal, Germany Uranium-plutonium mixed oxide fuels U 1-y Pu y O 2-x are currently studied within the frame of the fourth generation (GEN-IV) of nuclear reactors and more specifically for sodium-cooled fast neutron reactors (SFRs).

Pu: [Rn] 5f 6 6d 0 s 2 | Am: [Rn] 5f 7 6d 0 s 2 PuO 2 & AmO 2  M 5 N 7 line (M α or 3d 5/2 -4f 7/2 ) ΔE = 1.2 eV / 1.3 eV PuO 2  Pu +4 / 5f 4 6d 0 Electronic configurations considered : 5f 6.0  5f 4.0 / O 2p 4.0 2p 6.0 Energy -E 0 (eV) 5f 4.5 6d0 7 s 2 5f 4.4 6d0 7 s 2 5f 4.3 6d0 7 s 2 5f 4.2 6d0 7 s 2 5f 4.1 6d0 7 s 2 5f 4.0 6d0 7 s 2 PuO 2  5f 4.0  5f 4.5 Energy -E 0 (eV) 5f 5.6 6d 0 7s 2 5f 5.5 6d 0 7s 2 5f 5.4 6d 0 7s 2 5f 5.3 6d 0 7s 2 5f 5.2 6d 0 7s 2 AmO 2 Am M 5 E 0 = 3890.5 eV  5f 5.2  5f 5.6 Electronic configurations : 5f 7.0  5f 5.0 / O 2p 4.0 2p 6.0 Best results obtained for Am 5f Energy-E 0 (eV)

If charge transfer is only considered Np 5f -O 2p :

Calculations with 6d 0 (5f 3.0 -5f 4.0 )  no improvement The substitution of U by Pu atoms is observed in both U and Pu experimental spectra


Merzoug Soltane
email: merzoug-soltane@univ-eloued.dz

Derdour Makhlouf

Philippe Roose
email: philippe.roose@univ-pau.fr

Kazar Okba
email: kazarokba@gmail.com

PREDICTIONS & MODELING ENERGY CONSUMPTION FOR IT DATA CENTER INFRASTRUCTURE

Keywords: energy consumption, energy modeling, energy predictions, datacenter energy consumption I

Recent statistics of energy consumption by Cloud datacenter show the DCs consumes more and more energy each year .that crated big challenge in Cloud research. IT industry is keenly aware of the need for Green Cloud solutions that save energy consumption in Cloud DCs. A great deal of attention has been paid to minimize energy consumption in cloud datacenter. However, to understand the relationships between running tasks and energy consumed by hardware we need to propose mathematical models of energy consumption. The models of energy consumption can be help as to saving energy.

Both researchers aim to proposed mechanism for energy consumption. In this paper, we analyzed the relationships between Cloud system manager and energy consumption. This paper aims at proposing and designing energy consumption models with mechanism of prediction energy.

INTRODUCTION

Today, both researchers and engineers aim to minimize energy consumption based on energy modeling, because is one of the most important researches in Cloud computing [START_REF] Hooper | Green computing[END_REF].

However, the fast evolution of cloud datacenter created really problem of energy consumption. For example a typical datacenter needs 10 megawatts of power to operate [START_REF] Hooper | Green computing[END_REF], in 2013 [2], the Datacenter in the USA consumed 91 billion of electricity (kW /h) costing US businesses 13$ billion per year for electricity bills and generating nearly 100 million tons of carbon pollution (CO2) per year.

Nowadays, the modeling of energy consumption become very complicated because the scale of datacenter becomes larger and larger, the energy consumption of datacenter becomes bigger, each level of service cloud like (SaaS, PaaS, IaaS) has different energy consumption methods and these methods can't be used or migrated between each level of cloud.

Another problem of energy prediction in cloud datacenter make a lot of researchers and engineers eager to work on the development of predictive energy consumption mechanisms. However, in order to further predict future energy consumption researchers need to build energy model by their own. During the modeling of energy consumption, researchers need to capture the relationships between hardware resources and their energy consumptions.

In this paper, we analyzed the energy consumption of datacenter by each computing ressources, analyzed different energy consumption model, and analyzed performance on predicting energy consumption by all datacenter ressources. The contribution of the paper are as follows:

i. Proposed datacenter Rack power diagram to show 3 levels of power consumption ii.We analyzed related work of energy consumption and we proposed energy consumption models iii. We analyzed prediction energy consumption by all resources of datacenter and we proposed our prediction mechanism iv.We implemented first part of datacenter infrastructure in cloud simulation The rest of the paper is organized as follows, section 2 describes related work about energy prediction and energy modeling , In section 3, all detailed of our contribution .Experiments and conclusion are in sections 4 and 5 , respectively.

II. RELATED WORKS

To understand and control the energy consumption of a datacenter, we need first understand energy management mechanisms to identify all sources of energy consumption to help as to building energy model framework. This section of literature review covers and shows the different techniques in many axis of energy consumption model and energy prediction.

A. Energy consumption module

The difference between power and energy is very important, because reduction of the power consumption does not always reduce the consumed energy, multiple work have been done to model the energy consumption can be classified into (module for hardware components, software components) For processor energy consumption modeling work, first we find Shao et al. developed an instruction-level energy model for Intel Xeon Phi processor, which is the first commercial many core/multi-thread x86-based processor [START_REF] Shao | Energy characterization and instruction-level energy model of Intel's Xeon Phi processor[END_REF]. They developed an instruction-level energy model for Xeon Phi through the results obtained from an energy per instruction (Epi) characterization made on Xeon Phi.

In the same line of research we find, Kliazovich et al [START_REF] Kliazovich | GreenCloud: a packet-level simulator of energy-aware cloud computing data centers[END_REF] modeled the energy of server by power and frequency of CPU and a constant. In their model, all components expect for CPU where considered constant. Same authors in another paper [START_REF] Kliazovich | DENS: data center energy-efficient network-aware scheduling[END_REF], described an energy model of the whole datacenter, where computing nodes were liners model of CPU utilization, and switch was multivariate function. Another work, which is almost similar. Lee et al [START_REF] Lee | Energy efficient utilization of resources in cloud computing systems[END_REF] used the maximal power and minimal power, current power of CPU to model the energy consumption.

Up to now, we have focused on energy consumption models based on physical characteristics of the data center. Equally important is to consider the type of software application of a datacenter. After analysis, we can broadly categorized datacenter software into two categories: the Operating System/Virtualization layer and the applications.

There has been a number of works on datacenter application energy modeling, like Smith et al [START_REF] Smith | Cloudmonitor:Profiling power usage[END_REF].Described "Application Profiles," which is a means for presenting resource utilization of distributed application deployment. The Application Profiles they developed captures usage of the CPU, memory, hard disk, and network accesses. They described "Cloud Monitor," a tool that infers the energy consumption from software alone with computationally generated power models. Krishnan et al. [START_REF] Bhavani | VM power metering: feasibility and challenges[END_REF] proposed a VM power consumption model using two hardware PMC events such as instruction retired (inst ret/sec) and LLC(Last Level Cache) misses for modeling power consumption of CPU, and memory.

In another hand, it is important to understand the energy consumption of OS level. Did One such characterization was done by Li et al. [START_REF] Li | Run-time modeling and estimation of operatingsystem power consumption[END_REF], which characterized the behavior of a OS across a large spectrum of applications to identify OS energy profiles and proposed OS energy consumed model. The OS energy consumption profiling gave a breakdown of power dissipation of OS routines, this study show the multiple application execution are found to consume 50% of total power on the examined OS routines. The capacitive load to the network causes significant power consumption about 34%.

B. Energy prediction

In this sup-section, we present relevant approaches proposed in the literature for prediction energy. Some works of prediction energy addressed to optimize power consumption in cloud datacenter, Hieu et al [START_REF] Hieu | Virtual machine consolidation with usage prediction for energy-efficient cloud data centers[END_REF] proposed Virtual machine consolidation algorithm with usage prediction (VMCUP) for improving the energy efficiency of cloud data centers. The proposed algorithm executed during the virtual machine consolidation process to estimate the short-term future CPU utilization based on the local history of the considered servers. The joint use of current and predicted CPU utilization metrics allows a reliable characterization of overloaded and under-loaded servers, thereby reducing both the load and the power consumption after consolidation.

Farahnakian et al [START_REF] Farahnakian | LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers[END_REF] presented a linear regression based CPU usage prediction (LiRCUP), for VM migration. Specifically, the authors estimated the future CPU usage to predict overloaded and under-loaded hosts; then, some of VMs are migrated to other hosts before a SLA violation occurs. Consequently, such a solution relies on early migration of VMs even when the current resource usage of the considered hosts is still acceptable, thus resulting in unnecessary migrations.

In the same line of research, Dhiman et al. [START_REF] Dhiman | A system for online power predictionin virtualized environments using Gaussian mixture models[END_REF] proposed a power modeling methodology based on Gaussian Mixture Models that predicts power consumption by a physical machine running multiple VM instances. The proposed approach requires a training phase to perceive the relationship between the metrics of the workload and the power consumption. The authors have evaluated the proposed model via experimental studies involving different types of the workload. The obtained experimental results have shown that the model predicts the power consumption with high accuracy (<10% prediction error), which is consistent over all the tested workloads.

III. DATACENTER ENERGY MODELING

In this section, we describe how we organized resources of datacenter. We designed the flow of electrical power between all levels of datacenter (DC) resources fig

Level3

PROCESSOR POWER CONSUMPTION

Today, Modern processors such as Intel Xeon consist of multiple billions of transistors, which make them utilize huge amount of energy. Because of these, powerful CPU considered the one of the largest power consumers of a server, the major categories of processors used in current datacenter systems multi-core CPU.

We modeling CPU activity in server (s) associate with allocation new VM its consumption brought to the server. This value represents the increase consumption induced by the hosting of this new VM on this server.

MEMORY POWER CONSUMPTION

The memory is the second largest energy consumer in a server consumes about ≈30% of the total power. IT equipment such as servers comprise of a memory hierarchy. The rapid increase of the memory capacity and bandwidth has contributed for memory sub system to consume a significant portion of the total server power.

We modeling RAM energy consumed in server (s) associate with new request of allocation VM its consumption brought to the server. This value represents the increase consumption induced by the hosting of this new VM on this server.

P.Ram (Vm

COOLING FANS POWER CONSUMPTION

We modeling cooling fans energy consumed in server (s) related with application run in the server. This value represents the increase consumption induced by the hosting new run application on this server.

P.cool (S)

NETWORK POWER CONSUMPTION

Network interface is a significant contributor to the system power consumption, consume between (10% to 7%) can be either in idle mode or in active (either receiving or transmitting packets) mode, the total energy consumption of the network interface can be represented as We modeling energy consumed by network switch based on stat of each interface, we define tow stat for network interface (active interface, idle interface).

B. Our proposed mechanism of Prediction

The proposed model is a combination of a several sub models (VM resource tracing, workload controller, management system resources and power tracing, base model of prediction, energy calculation model be on our proposed model of energy consumption). To estimate the energy consumption from our predicted mechanism and the remaining measurable parameters of energy consumption. We give detailed overview of the proposed model with its inputs and outputs given in Figure 3.

Algorithm of prediction

Figure 4: chart of prediction algorithm IV. IMPLEMENTATION AND EVALUATION To evaluate performance, we simulated our algorithm and model of energy consumption using the ICan Cloud simulator, which is a graphical runtime environment for network simulation. We simulated scenario for allocating resources subsequently, we implemented one cloud computing service provider to offer cloud services to users, and the datacenter implemented for this cloud runs many servers as shown in table 1. C++ used to evaluate the performances of our prediction strategies. We tested them with a number of user requests to show the performances of our algorithm, such as global energy prediction and energy consumption Figure 5 shows the energy consumption rate compared with energy prediction rate. When we apply our proposed algorithm in this work, we observe the remarkable good prediction of energy consumed. This result of prediction shows the performance of our model of power consumption and our prediction strategy and confirms our choice to use modeling approach to measure the energy needed to ruining datacenter resources.

V. CONCLUSION

We introduced the problem of power consumption by cloud datacenters. Then, we presented in this paper modeling approach to measured energy consumed by each resources in datacenter. We combined prediction strategies with our model of power to calculate power consumption. The simulation aims to clarify the efficiency of the internal function (tracing energy consumed by each resources), and the external function (the energy consumption and energy predicated). In the future, we plan to develop more models of energy consumption with a version of the prediction algorithm with more intelligence.

  1. A more detailed presented in Fig 2 the latter figure provides an overview of the organization of DC energy consumption modeling.

Figure 1 :

 1 Figure 1: Rack power flow in datacenter  Level 1: contained rack installed in a datacenter.  Level 2: contained servers, storage server, and network equipment.  Level 3: contained all resources like (CPU, RAM, Disk, Switch,).

Figure 2 :

 2 Figure 2: Rack power flow in datacenter A. Model of power consumption In this sub-section, we describe detailed of our models of energy consumed in each level Level1 (Rack power) P. rack = power of all resources installed in the rack = (P.CPU+P.RAM+P.CoolingF) + (P.Disk) + (P.Switch) Level2 (power consumption for server) The energy consumed by the server S computes according to the following formula P.Server = All energy consumed by all components of this server = (P.CPU) + (P.RAM) + (P.CoolingF) Model of power consumption for storage components P.Storage= (P.ActiveDisk*M)

P

  .Cpu (Vm.s): represents the energy consumed by CPU when server S allocated new VM P.active (S): represents power consumed by active CPU in server S P.idel (S): represents power consumed by inactive CPU in server S CPU max(s): represents max power consumed by CUP in server S CPU (Vm): represents allocated CPU in new virtual machine µ: CPU utilization rate

  .s) :represents the energy consumed by RAM when server S allocated new VM P.active (S): represents power consumed by active Ram in server S P.idel (S): represents power consumed by inactive Ram in server S RAM max(s): represents max power consumed by RAM in server S RAM (Vm): represents allocated RAM in new virtual machine ∂: RAM utilization rate.

  = represents the energy consumed by cooling system in server S P.activeFans= represents the energy consumed by each active fans in server S P max(s): represents max power consumed by server S STORAGE POWER CONSUMPTION Hard Disk is currently the main type of storage media used in data center servers. The rapid increase of the hard disk capacity impact positively on mass storage of data center servers hi can storage to 20 Terabit. We modeling energy consumed by storage capacity in server (s) related with the allocation of new VM. P.storag (Vm.s): represents the energy consumed by storage when server S allocated new VM P.activeDisk (S): represents power consumed by active disk storage in server S Storage (Vm): represents storage capacity allocated for a new virtual machine P max(s): represents max power consumed by server S

Figure 3 .

 3 Figure 3. Detailed of model of prediction for energy consumption  VM resources tracing: This model tracks the state of all virtual machines allocating in the server. More precisely this model follows the resources allocated for each VM and control their energy consumption.  Management system resources and power tracing: This model follows the state of all PMs. More precisely, this model tracks the physical resources installed and allocated in each server and controlled the energy consumption by each PM.  Workload controller: this model control all allocation request. Moreover, send information of each allocation request to VM resources tracing model  Energy calculation model: this model based on our energy consumption model.  Base model of prediction: this model base in algorithm of prediction to give estimated energy consumption by the system.

Figure 5

 5 Figure 5: simulation result

TABLE I .

 I

				A. SIMULATION RESULTS
			DATACENTER RESOURCE
	N°DC	Rack	Datacenter resource Server	storage
			Cpu dual
	1	100	core	1To
			Ram 16 g