Violaine Villebonnet

Georges Da Costa

Laurent Lefevre

Jean-Marc Pierson

Patricia Stolf

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

As the number of data centers increases, it is urgent to reduce their energy consumption. Although servers are becoming more energy-efficient, their idle consumption remains high, which is an issue as data centers are often over-provisioned. This work proposes a novel approach for building data centers with heterogeneous machines carefully chosen for their performance and energy efficiency ratios. We focus on web applications whose load varies over time, and design a scheduler that dynamically reconfigures the infrastructure, by migrating applications and switching machines on or off, so that the energy consumed by the data center is proportional to the load. Experiments evaluate the approach and show the energy savings achieved by our heterogeneous data center design and management while satisfying Quality of Service (QoS) constraints.

I. INTRODUCTION

IT infrastructures, especially data centers, are responsible for a substantial amount of the global energy consumption and greenhouse emissions. While data centers consume a lot of energy, they are often over-provisioned and contain numerous servers that are not fully utilized. An Uptime Institute survey [1] suggests that close to 30% of servers in US enterprises' data centers are comatose, meaning that they are consuming power without doing any work. It has been shown that a typical server, when idle, can consume up to 50% of the amount drawn at peak utilization [START_REF] Barroso | The Case for Energy-Proportional Computing[END_REF]. Therefore we need novel approaches for conceiving and managing data centers more efficiently.

This work provides an approach for designing data centers whose energy consumption is proportional to their load. It focuses on services with variable load, and offers means for adjusting the computational capacity to service requirements by dynamically modifying the number and type of machines hosting the service. The goal is to minimize the energy consumed by allocating the minimum number of resources required to meet application demands. We advocate the use of multiple server architectures that feature heterogeneous ranges of performance and energy consumption. Each machine type is profiled by running the target application, and the best resources combinations for all application performance rates are computed. Our previous work introduced the concept of heterogeneous energy proportional infrastructure, named "Big,Medium,Little" (BML) [START_REF] Villebonnet | Big, Medium, Little": Reaching Energy Proportionality with Heterogeneous Computing Scheduler[END_REF]. The present work extends it by providing a scheduler that handles dynamic reconfiguration decisions, which consist in dynamic resources management with switch on and off actions, whose time and energy overheads are taken into account, to achieve energy proportionality while respecting QoS requirements.

II. RELATED WORK ON ENERGY PROPORTIONALITY

Barroso and Holzle [START_REF] Barroso | The Case for Energy-Proportional Computing[END_REF] introduced the goal of energy proportionality in 2007. They discovered that servers' average utilization was between 10 and 50% in a Google data center. The issue is that a typical server is not very energy efficient under low utilization as its idle power consumption can amount up to 50% of its peak consumption. In [START_REF] Varsamopoulos | Trends and Effects of Energy Proportionality on Server Provisioning in Data Centers[END_REF], Varsamopoulos et al. define two metrics to quantify energy proportionality: IPR, for Ideal to Peak Ratio, which measures the dynamic power range, and LDR, for Linear Deviation Ratio, to evaluate the linearity of the consumption. They studied the evolution of energy proportionality and found that recent servers feature better characteristics, but most time it only concerns one aspect: a larger dynamic power range or an improved linearity.

Attempts have been made to improve power management of servers, such as Running Average Power Limit (RAPL) by Intel. Via this mechanism a user can specify a power consumption threshold that the processor will not exceed for a given period. Energy savings achieved by RAPL have been evaluated for data stores applications [START_REF] Subramaniam | On the Energy Proportionality of Distributed NoSQL Data Stores[END_REF], and latency critical workloads [START_REF] Lo | Towards Energy Proportionality for Large-scale Latencycritical Workloads[END_REF]. This power capping tool offers better energy proportionality, but does not help reducing idle consumption.

An example of heterogeneity is ARM big.LITTLE [START_REF] Big | LITTLE: The Future of Mobile[END_REF] that gathers on the same board two different processors:a low power processor that delivers low level performance, and a more powerful one, consequently consuming more, to process intensive tasks. ARM developed this technology to extend mobile devices' battery life. The concept has been adapted to server scale in [START_REF] Wong | Scaling the Energy Proportionality Wall with KnightShift[END_REF]. Authors designed a motherboard containing a server processor, called primary server, and a low power processor, called the Knight, which is always on and wakes up the primary server in case of high load.

Our work aims at achieving energy proportionality at the data center scale by combining existing heterogeneous architectures. The strong aspect is that it does not rely on a specific processor design. This idea has been introduced previously [START_REF] Costa | Heterogeneity: The Key to Achieve Power-Proportional Computing[END_REF], and its feasibility has been studied [START_REF] Villebonnet | Big, Medium, Little": Reaching Energy Proportionality with Heterogeneous Computing Scheduler[END_REF]. The present work implements scheduling policies that take into account both benefits and drawbacks of using independent machines.

III. CHARACTERIZING THE APPLICATION AND ITS LOAD

Our system considers applications with variable load and it adapts the infrastructure to load conditions so that energy consumption more closely matches resource utilization. For such, the application performance is characterized using an application metric that represents the amount of work performed over a given time unit. This metric is used to assess the application performance independent of the underlying architecture and to determine the QoS. With respect to performance, applications can be classified as critical, having strict performance requirements, and tolerant, applications with soft QoS requirements. Critical applications can be found in banking and medical areas where delays have serious consequences. More tolerant applications are found in, for instance, enterprise services, or services with flexible deadlines. Applications can lie in between these classes, and hence intermediate classes can be required depending on the use-case.

Applications are also classified on whether they can be migrated across machines, and whether they can run on multiple architectures. The former is determined by how the application maintains state and on the amount of data to transfer. We must evaluate the application's migration overhead, both in terms of duration and energy consumption. Another important characteristic is the application malleability, i.e., its ability to be distributed across several machines. If not, the minimum and maximum number of instances should be specified. This criterion poses a constraint when computing the possible hosting machine combinations.

The knowledge of how load evolves, an important parameter in our system, can be perfect, when the load can be determined with precision; partial, where certain characteristics are known, such as weekly, diurnal, hourly patterns, but the accuracy of variations is unknown; and unknown when no a priori information is available, and the load must be predicted.

IV. BUILDING BML INFRASTRUCTURE IN 5 STEPS

This section lays out the steps towards selecting hardware for our heterogeneous energy proportional data center. Four illustrative architectures are used as input, however this methodology is not dependent on the number of architectures. Results with real hardware are presented in Section V.

A. Step 1: Characterizing Each Architecture Profile

The first step consists in building the energy and performance profiles for all available hardware considering the target application. The profile of an architecture i provides:

• idleP ower i : average idle power of architecture i, in Watts.

• maxP erf i : maximum performance rate, expressed with the application metric (e.g., nb of requests processed per second).

• maxP ower i : average power consumed when it reaches maxP erf i rate, expressed in Watts.

A function is created using this data to compute the power consumed by architecture i for a given performance rate perf Rate. We make the assumption of linear power consumption, even if we know it might lead to small underor over-estimation, as studied by Rivoire et al. [START_REF] Rivoire | A Comparison of High-level Full-system Power Models[END_REF]. Yet, this approximation is precise enough for our solution, and eases the profiling phase. Although acquiring more intermediate data points, if the application allows, would enable more precision, our methodology would not be affected.

We consider that enough machines of each type are available to choose from when building machine combinations, which enables creating ideal combinations. With minor changes, this work can consider cases of existing heterogeneous infrastructure where there is limited numbers of machines of each type.

B. Step 2: Sort Architectures to Keep Only BML Candidates

Building a BML infrastructure starts by sorting machines by decreasing maximum performance. Then we check that their respective maximum power consumption respects this initial ordering. We proceed by comparing sorted architectures in pairs; if one has lower performance than another while consuming more energy, we remove it from the BML candidates as it would not improve energy proportionality.

Figure 1 gathers the profiles of illustrative architectures A, B, C, and D. Beyond the point (maxP erfi, maxP oweri) of each architecture, its profile is repeated to picture multiple nodes. In this case, only three architectures are kept as good candidates for a BML infrastructure, namely A, B and C. Architecture D is discarded because its maximum power consumption is greater than A's, the most powerful machine. Once the initial filtering is done, architectures can be labeled according to their performance as Big, Medium or Little. In this example: A ← Big, B ← M edium, and C ← Little.

C. Step 3: Finding Crossing Points between Architectures

This step determines how architectures should be combined for more power proportionality. We define the minimum utilization threshold for each architecture, expressed with the application performance metric. For two architectures, i as Little and j as Big, the minimum threshold of architecture j corresponds to the point from which utilization of j becomes more relevant than i's considering power consumption. Except for the Little architecture whose minimum threshold is 1, a function is needed to compute thresholds for other architectures. The points where an architecture becomes preferable over another are termed as crossing points as they represent the points where power profiles meet.

Left part of Figure 2 illustrates this step with architectures A, B and C, now denoted Big, M edium and Little. Utilization threshold of M edium starts around a performance rate of 150. Before this point, it is more efficient to use up to five Little nodes. The minimum utilization threshold of Big corresponds to the maximum performance rate of a M edium node. This results in a substantial jump in power consumption when switching from M edium to Big since this crossing point is not optimal, and next step is needed to improve it.

D. Step 4: Finding Crossing Points between Architectures and Combinations of Smaller Architectures

This step is required for more than two architectures. The previous step computes the crossing points between homogeneous combinations of machines, but with three architectures one must determine whether adding Little nodes to M edium combinations help improve power proportionality and reduce the gap between M edium and Big architectures. Right part of Figure 2 pictures the re-evaluated crossing points and shows that minimum threshold of Big has consequently increased.

E. Final step: Computing Ideal BML Combination

This step computes the ideal machine combinations and their corresponding power consumption for a given performance rate. Building a BML combination is similar to a bin-packing problem where architectures and their maximum performance represent bins of different sizes. The singularity of our problem is that there is only one object to pack, i.e., target performance, but it can be divided into any pieces of any size. Bins are now sorted by size and cost, so what is left to perform is to divide the desired performance into pieces to fill them. Firstly, we consider architectures sorted decreasingly and seek to fill completely Big nodes, then M edium, and so on. Architectures are the most energy efficient when fully loaded. Secondly, we use minimum thresholds to choose the right architecture to process the remaining performance.

V. EXPERIMENTAL EVALUATION

A. Experimental Setup and Profiling Results

The processors we have chosen to profile are the following: Paravance: x86 Intel Xeon E5-2630v3 (2x8 cores); Taurus: x86 Intel Xeon E5-2630 (2x6 cores); Graphene: x86 Intel Xeon X3440 (1x4 cores); Chromebook: ARM Cortex-A15 (1x2 cores); Raspberry: ARM Cortex-A7 (1x4 cores). A WattsUp?Pro wattmeter monitors power consumption of the Samsung Chromebook and Raspberry Pi2B+. The x86 servers are available at Grid'5000 [START_REF] Bolze | Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed[END_REF], a French experimental testbed for research, whith power monitoring data accessible via Kwapi [START_REF] Rossigneux | Generic and Extensible Framework for Monitoring Energy Consumption of OpenStack Clouds[END_REF].

A stateless web server is our target application because: • a load balancer could allow the load to be distributed among several web server instances;

• being stateless, it can be easily migrated by stopping a server instance and launching a new one on the destination machine, and then updating the load balancer;

• it is a perfect example of application with variable load over time, and its performance can be characterized with an application metric: number of requests processed per second.

We use lighttpd as web server and Siege as web benchmark tool. The content of the web server is a python cgi script. Each request consists in a loop of random number generation, while loop iterations is also chosen randomly between 1000 and 2000. The request response is a static html page containing this later integer. We execute the benchmark with an increasing number of concurrent clients in order to find the maximum request rate that can be processed. Each test runs for 30 seconds and the maximum performance is the average of 5 results. We also measure On/Off durations and energy consumption. Table I and Figure 3 present the results.

B. Results at Server-Scale

Figure 3 is the result of Step 1 described in Section IV. Step 2 results in the removal of Taurus architecture as its maximum power consumption is higher than Paravance's while delivering lower performance. Step 3 realizes that the profile of Graphene never crosses any other architecture's profile, and consequently it is removed from the list of candidates. Our final heterogeneous infrastructure comprises Raspberry (Little), Chromebook (Medium) and Paravance (Big). Their minimum utilization thresholds are respectively 1, 10 and 529 requests per second. The resulting ideal BML combination is depicted in Figure 4. Big architecture's profile is also represented to demonstrate the gains of the heterogeneous combination. In addition, we introduce BM L linear whose idle power is equal to Little's and maximum power and performance is equal to Big's. It represents an achievable goal, and how our solution approaches it.

C. Comparison with Lower and Upper Bounds

To evaluate our approach, we developed a simulator in Python, which takes as input the experimental machine profiles, and a trace file describing the application load variation over time. We emulate a load prediction mechanism by considering a sliding look-ahead window. We use as prediction the maximum load value over a window of 378 seconds, equivalent to 2 times the longest On duration. The scheduling policy is pro-active; at each prediction it computes the corresponding BML combination, and if this leads to a new hardware configuration, a decision of reconfiguration is taken. During the reconfiguration, no other decision can be made, ensuring the completion of On/Off actions before a new decision. The next prediction window starts from reconfiguration completion time, but if there is no combination changes, the window just slides one time step forwards, a second in this case.

We compare this placement algorithm against a theoretical BML lower bound and two homogeneous upper bounds corresponding to existing data center management. We run the simulations for days 6 to 92 of 1998 World Cup traces (available online). The resulting scenarios are as follows:

• UpperBound Global: a homogeneous data center with constant number of servers, computed according to the maximum request rate. Here it contains 4 Big machines always On. This is an example of a classical over-provisioned data center.

• UpperBound PerDay: a homogeneous data center, but dimensioned each day according to the daily maximum rate. This is an example of coarse grain capacity planning.

• Big-Medium-Little: our BML infrastructure and placement algorithm. The total consumption per day contains the energy consumed by computation and by On/Off reconfigurations.

• LowerBound Theoretical: the minimum computing energy achievable with BML infrastructure if it is dimensioned every second with the ideal combination. This is an unreachable lower bound considering no On/Off latency and energy costs, picturing the best energy proportionality we could reach. Figure 5 shows that our solution is very close to the theoretical lower bound: on average over 86 days, it consumes 32% more energy than the lower bound, minimum 6.8% and maximum 161.4%. This graph demonstrates the high static costs of classical over-provisioned data center, and shows the energy proportionality achieved by our solution.

VI. CONCLUSION AND PERSPECTIVES

We demonstrated the feasibility of BML infrastructure with existing hardware, and evaluated its performance when hosting stateless web servers. We showed that compared to classical data center management, BML drastically reduces static costs and consequently achieves energy proportionality. As future work we will investigate the impact of load prediction errors on reconfiguration decisions. It is also worth considering other hardware combinations than pre-computed BML combinations as reconfiguration possibilities, and take in account their corresponding overheads when taking reconfiguration decisions.

Fig. 1 .

 1 Fig. 1. Architectures A, B and C are good candidates for BML infrastructure, but D will be removed due to its poor energy efficiency compared to A.

Fig. 2 .

 2 Fig. 2. On left: Step 3 -First step of crossing points computation between Little and M edium, and between M edium and Big. On right: Step 4 -Second step of crossing points computation between Little and M edium, and between combinations of M edium -Little and Big

Fig. 3 .

 3 Fig. 3. Power and performance profiles of web servers acquired from experiments on 5 different architectures

Fig. 4 .

 4 Fig. 4. Consumption of BML combination over an increasing performance rate, until maxP erf Big , compared to Big and BM L linear

Fig. 5 .

 5 Fig. 5. Energy consumption comparison with lower and upper bounds.

TABLE I PERFORMANCE

 I AND POWER PROFILES OF EACH ARCHITECTURE.

	Architecture	MaxPerf Idle-Max Power Ont	On E	Offt	Off E
	Codename	(reqs/s)	(Watts)	(s)	(Joules)	(s)	(Joules)
	Paravance	1331	69.9 -200.5	189	21341	10	657
	Taurus	860	95.8 -223.7	164	20628	11	1173
	Graphene	272	47.7 -123.8	71	4940	16	760
	Chromebook	33	4 -7.6	12	49.3	21	77.6
	Raspberry	9	3.1 -3.7	16	40.5	14	36.2

ACKNOWLEDGMENTS

Experiments presented in this paper were carried out on Grid5000 testbed, supported by a scientific interest group hosted by Inria, including CNRS, RENATER, several Universities and organizations (www.grid5000.fr). This research is partially supported by the French ANR MOEBUS project.


Christina Herzog
email: herzog@irit.fr

Jean-Marc Pierson
email: pierson@irit.fr

Laurent Lefèvre
email: laurent.lefevre@ens-lyon.fr

A Multi Agent System for Understanding the Impact of Technology Transfer Offices in Green-IT

We present a multi agent system simulating the complex interplay between the actors of innovation involved in the development of technology transfer for Green IT. We focus on the role and the influence of technology transfer offices on the individual objectives of each other actor (researchers, research facilities, companies). We analyse also their impact on several parameters, including sustainability.

Introduction

Technology Transfer Offices (TTO) have been promoted in many research centers and universities as a mean for accelerating the adoption of research in the industry. While it seems obvious that these dedicated offices might have a positive role on the effective transfer, we did not see any computerized model of their impact. It would be too ambitious to analyse it through the wide spectrum of science, hence we focus on Green IT, the part of Information Technologies interested in environmental aspects, besides social and economical ones.

In the last decade, research on energy savings in IT has become important for both industry and academics. Several studies conducted by international organisations warn about the steady increase of energy consumption in various fields such as Datacentres, Cloud Computing [START_REF] Cook | Clicking clean: How companies are creating the green internet[END_REF], raising concerns on economic, social and ecological aspects. In laboratories techniques have been developed and show promising results in terms of energy consumption reduction. Unfortunately, the transfer (or even the knowledge) of these techniques to industries is limited. To understand the (positive or negative) impact of the presence of TTO on Green IT development, we analyse it through a simulation conducted with a multi agent system (MAS). The MAS helps to define the different actors involved in the technology transfer (including researchers, companies, funding agencies), and to observe the evolution of their objectives when parameters of the simulation evolves (external funding, company policies, ...). Previously we proposed [START_REF] Herzog | Modelling technology transfer in Green IT with Multi Agent System[END_REF] the concept of a generic multi agent system for technology transfer, where we observed the evolution of the actors objectives. In this work we design the implementation of this MAS and its extension to integrate TTO.

The main contributions of this research work are:

-An agent based model of technology transfer between researchers, companies, TTO and its implementation; -An analysis of the TTO's presence and its impact on individual objectives of actors and on the overall objective of sustainability.

The article is organized in the following way: We present in Section 2 the multi agent system including the description of the selected actors and their links, the sustainability concept in Section 3 and the implementation of the model with NetLogo in Section 4. We present simulation results in Section 5 before state of the art in Section 6, and we conclude in Section 7.

Multi agent System for Technology Transfer

In [START_REF] Herzog | Modelling technology transfer in Green IT with Multi Agent System[END_REF] we have conceptualized a MAS for modeling the relationships between innovation actors in terms of technology transfer. The MAS has been built from a literature review and a detailed analysis of 80 responses to a survey sent to colleagues in the field of Green IT field. Their motivations and their links in the context of a transfer from research facilities to industry (and vice versa) were analyzed. The main players in the technology transfer, their general goals and their means of action were extracted and detailed.

In this paper, we detail the implementation of the MAS, focusing on researchers, research facilities, companies and TTO, together with their links. The full system adds funding agencies, standardization bodies, lobbying groups, governments and business angels in [START_REF] Herzog | Contribution to the modeling of technological transfer in Green IT using multi-agent-systems[END_REF]. This simplified presentation highlights the keys for the transfer and is not exhaustive. Note finally that only the activities of the actors in direction of Green IT have been taken into account (leaving aside individual non related goals).

Researchers, Research Facilities and Companies

First concerned by technology transfer, they produce knowledge through publications that they seek to increase, and by building up projects with companies through their research institute. In the scope of this work, their primary goal is the number of publications where publications are related to their connections they create at conferences and / or collaborative projects, and the financial budget of their research centre. More links leads to more opportunities for publications. Researchers may be permanent or non-permanent. Non-permanent researchers (PhD candidate, PostDoc, ...) are supervised by permanent (and their number is limited by the number of permanent researchers) and have a limited contract duration.

The researchers are grouped in research facilities. Research facilities contribute to the technology transfer by giving incentives to researchers for publications. However they have their own objective (their reputation) that pushes them to build external contracts: collaborative projects (in a consortium) or direct cooperations with companies. The research facility's reputation is computed as the moving average (within a sliding window of 3 years) of its researchers' publications together with its contracts. Research facilities hire non permanent researchers within contracts.

Companies look to increase their profits by taking a competitive difference. Their goal is to increase their share in the market and therefore to increase their turnover. Participation in collaborative projects increase leadership when the project is successful, but requires initial investment that might be lost. They hire new employees to participate in projects. They dedicate a portion of their sales to research and development.

Technology Transfer Offices

Technology Transfer Offices (for instance SATT-Technology Transfer Anonymous Society-in France, PSB in Austria) are structures associated with research facilities, intended to facilitate and accelerate technology transfer. Their goal is to increase their turnover (and therefore that of their public shareholders) taking part in the implementation of contracts. In return they provide a permanent support and address book helping research facilities and businesses to contract better. While a contract between a research facility and a company lasts for a limited duration, the existing link through a TTO lasts beyond the duration of the contract. Also, the chances of finding a market for a research is higher when a TTO exists, increasing the chances of contracts for the associated research facility. In this work we will investigate in particular the impact of their presence on the goals of each of the other actors, but also on a general goal for improving society's sustainability.

The Concept of Sustainability

A presented in [START_REF] Costanza | Defining and predicting sustainability[END_REF], sustainability is a concept defined by the conjunction of three factors: environmental, social and economic. In a simplified way, an actor of a system improves its sustainability if at least one of these increases when the others do not decrease. As an example, in the field of Green IT, a more recent material often uses less electricity for the same computing power, but at the same time its production, transport, the recycling of the older equipment, all have a negative impact on the environment [START_REF] Hilty | Essays on the Relationship between ICT and Sustainable Development[END_REF]. The SPI (Sustainability Performance Indicator) for each actor has 3 factors, weighted at 33% each: ecological, societal, economical. Each factor is itself dependent on several elements weighted differently. Full details of our proposal for the calculation can be retrieved in [START_REF] Herzog | Contribution to the modeling of technological transfer in Green IT using multi-agent-systems[END_REF].

The ecological factor is reflected by (i) the awareness of green IT solutions by the actor and (ii) by its efforts in terms of Reduce, Reuse and Recycle, typically during collaborative projects.

The social factor shows the actor's role in society: (i) green employment represents the employees recruited to work on (green IT) contracts. Employment is an important aspect for social evolution in a society. (ii) the society's knowledge on IT consumption. Reports like [START_REF] Cook | Clicking clean: How companies are creating the green internet[END_REF] help develop this, and is based on studying the publications from researchers and press releases from companies. (iii) Rethink is the ability of an actor to rethink its green IT in terms of strategy. For instance, a company with more contacts can rethink its strategy better, while a researcher has more freedom of thoughts when not involved in contracts. (iv) the actor's image in society is dependent on its communication strategy, mainly based on contracts and publications. (v) the influence of an actor on the standardization organizations. Big companies have more influence in organizations like ISO or IEC than individuals.

Finally, the economic factor is valued by: (i) the economic impact of green solutions. It tracks the successful contracts, meaning ones that generated benefits. (ii) the turnover representing the richness of each actor. (iii) the attractiveness of an actor for investors is an important factor for the economic dimension since it allows companies, TTO and research facilities to grow their turnover, indirectly increasing their R&D efforts.

All these values evolve with actors' actions and time. For instance, the awareness of Green IT solutions increases with the number of publications and contracts and decreases as time passes.

Implementation of the Multi Agent System

Netlogo as a multi agent system framework

We implemented a multi agent system with NetLogo 5.3.1 [START_REF] Wilensky | NetLogo itself[END_REF]. NetLogo simulates the evolution and interaction of agents in complex systems. NetLogo was created in 1999 by U. Wilenski, and is regularly updated (last version in 2015) [START_REF] Wilensky | Introduction to Agent-Based Modeling[END_REF]. It is used in many scientific fields: social science, economics, commercial distribution, biology, modeling complex behaviorrs in a population, etc. In NetLogo we used the agents turtles, links and observers. The turtles represent the actors in our world, the links are their connections. Observers collect the information used for statistics. Each agent operates independently in discrete time-steps.

Representation of the actors and their evolution

Each agent has its own attributes, those values change with interaction and time. A researcher may be permanent or not. If they are not permanent, they are associated with a contract those duration is given (ttl for time-to-live). It may be extended in case of successful collaborations. Researchers are members of a research facility. This is represented by a link between them (see Sec. 4.3).

As explained earlier, the primary goal for a researcher (as seen in this work) is to publish and therefore should have an attribute reflecting this. However, this attribute is shared with other actors, like all the attributes given here: each actor is active in the system at different regular intervals (action period). Contracts and publications are stored, as well as the interests of one actor for 3 technologies (cloud, virtualization and cooling) having potential for energy reduction. These interests are used to compute a compatibility between actors when trying to collaborate (euclidian distance is used).

Research facilities have a turnover (richness), an amount dedicated to green research, a reputation (sliding value over 3 years), and its active research contracts at any point in time (used for linking the contracts with the non permanent researchers). If a TTO is attached to this research facility, a link is set up (see Section 4.3).

The TTO have a turnover, the number of transactions they were involved in and the percentage they take on every contract. Finally, the companies have a turnover and a R&D budget, and a number of employees in R&D. Funding agencies were modeled simply by regularly launching funds to initiate projects.

Once the actors defined, their evolution is controlled by algorithms called every time step. In our model, a time step (ticks in NetLogo) is equal to one day. The algorithm for a researcher is given in Algorithm 1. if r < 50 then p = partner-choice 17:

Algorithm 1 Evolution of a researcher agent

else if r < 75 then p = tto-choice 18:

else p = one-of find-neighbors-of-neighbors 19: make-link p "regular"

Each day, if it is not a permanent, its ttl is reduced (line 1). If it becomes zero, this researcher is removed from the system (line 2). Then, for each of its regular neighbors (loop in lines 5 to 9, definition of regular neighbors in Section 4.3), and if the research facility employing this researcher has sufficient funds dedicated to research (1000 in this case), then there is a probability of publishing with a neighbor (on average every 3 months with a probability of 20% -acceptation rate-). In this case, the research centre funds (1000) the publication (line 9). The number of publications by the researcher is updated (line 10). Contacts with neighbors can disappear (line 11, on average every 6 months), but also appear (lines 12 to 20). When a maximum of neighbors is not reached (line 12, this value is set to 10 in our experiments), a new contact might be established (a minimum of 3 months, line 14). As a function of the employer's performance, a bonus for creating new links is given (line 13): If a research facility has some contracts and/or is rich (high turnover), the bonus is increased and the new contacts are built more often. When a contact is built up, the question is with who should this researcher be linked: A survey we conducted within the Green IT community [START_REF] Herzog | Contribution to the modeling of technological transfer in Green IT using multi-agent-systems[END_REF] has shown that new contacts are established 50% randomly with other researchers and businesses, favoring the compatibility of interests (line 16, function partner-choice). 25% come from the social network (line 18, function find-neighbors-of-neighbors). TTO helps to establish new contacts in 25% of the cases (especially with companies, line 17, function tto-choice).

The algorithm for a research facility is the following (details omitted by lack of space): First it updates its interests (average of those of its researchers) and its reputation. If the budget is critical, the research facility finishes the contract of some non-permanent researchers, and then it pays the remaining ones. Then, it will act depending on its action period. If the budget is comfortable, it will hire a non-permanent (for one year, to a maximum of 4 times more non-permanents than permanents) and it will dedicate an incentive as a percentage of its turnover to research. Finally, it tries to launch a collaborative project. These are projects that will create technology transfer, based on their success.

The algorithm for the project creation is too long to be detailed here: a research facility seeks to form a consortium (between 3 and 6 partners) according to its own links, links of its researchers and those of its TTO, if existing. The other research facilities and companies can be partners if all their permanent researchers are not already in projects. If the project is accepted (20% chances), research facilities and companies receive a share of the funding (a 50% fraction of what is taken as overhead costs), they hire non-permanents during the project (maximum 36 months, depending on the share received), and companies invest what they receive. For all research facilities where TTO are present, these TTO take a percentage of the share (20% in the experiments). Finally, links (project, see Section 4.3) are created between all partners.

For a company, the algorithm is quite similar to that of a research facility except that it tries to create a direct partnership with only one research facility (links partnership). The algorithm of TTO's evolution is simple. If it is time to act, it transfers a back percentage amount to its research facility (a percentage of its turnover).

Representation of links and their evolution

We have defined 5 kind of links: (i) regular: contacts between researchers (from research facilities and companies) ; (ii) project: relationship between a research facility and a project consortium ; (iii) partnership: contract between a company and a research facility ; (iv) belongto: link between a research facility and its researchers; (v) ttolink: link between a research facility and its TTO.

The project and partnership links refers the collaboration's characteristics. Each link has its own attributes. For such link, the attributes are: original investment for companies, strength of collaboration linked to the compatibility between the partners of the link, number of contracts and turnover generated by the link, lifetime of the link, number of researchers in the research facility and company sides involved in this link, and finally the contract number.

Like all agents, links evolve at every time steps. For non-permanent links, the lifetime is reduced by one at each time step. When the lifetime is zero, if it is a project, it is finished and the permanent researchers of each partner of the consortium are available for new contracts. As a function of conversion rate (conversion is success-or failure of a project), it gains a profit up to 4 times the initial cumulative investment. This represents the success of a project and reflects the patents that may arise from the project. If it is a partnership, the principle is the same as for project except that only one company and one research facility are involved. Moreover the partnership will be extended for one year if it was generating a benefit (favoring the efficient partnerships, also controlled by conversion rate). Other links disappear.

Experiments

Methodology and objectives of experiments

The proposed multi agent system is complex (about 2000 lines of code), and has a large number of parameters. We varied its main parameters in extensive experiments. We give here the results of a representative subset showing the impact of the TTO presence, the others can be found in [START_REF] Herzog | Contribution to the modeling of technological transfer in Green IT using multi-agent-systems[END_REF]: number of TTO, number of companies, back percentage rates (the percentage a TTO returns to its research facility). When not specified otherwise, back percentage rates is set to 30% in the experiments. The action period for research facilities is set randomly (uniform) between 3 and 9 months, while for companies it is between 1 and 2 years, and for TTO it is between 1 and 3 months.

We compare the results on the objectives of each actor and on the SPI value. In the selected experiments we selected the following indicators:

publications: the sum of the publications of the permanent researchers reputation: the mean reputation of the research facilities -SPI : the mean value of the SPI value of research facilities and companies

The studied system simulates 10 research centres, 50 permanent researchers. We vary the number of TTO (from 0 to 10, i.e with 10 all research centers have a TTO) and companies (from 10 to 50). Larger experiments were conducted but did not give more lessons while significantly increasing the simulation time. Each experiment simulates 3640 days (10 years). We present boxplot values for 50 experiments with the same parameters.

Results

Figure 1(a) shows the impact of the number of TTO (x-axis, from 0 to 10) on the publications of the researchers. In this graph we see that the number of publications increases with the number of TTO. This exhibits the positive influence of TTO on the work of individual researchers. It increases also with the number of companies, whatever the number of TTO. This is expected since more companies means more potential contacts for researchers, leading to more publications. In Figure 1(b), we studied the direct influence of TTO on the research facilities they are attached to: on the left the reputation of research facilities without TTO, on the right with TTO. One can observe that, the reputation for research facilities with TTO is higher than the one for research facilities without TTO, whatever the number of TTO and companies. Here also the values are quite insensitive to the TTO number. This indicates that the influence of TTO on companies and sustainability is not important in our simulation. Finally in Figure 2(b) we show the mean reputation of research facilities as a function of the back percentage experiment with 4 TTO and 20 Companies. We observe again the positive influence of TTO on research facilities, but the influence of back percentage is negligible, indicating that the presence of the TTO is more important that the percentage it returns (see also in parallel Figure 1(b)).

Related Work

Few studies have focused on modeling technology transfer and links between the actors. A review article [START_REF] Kiesling | Agent-based simulation of innovation diffusion: a review[END_REF] is interested in multi agent systems for the diffusion of innovation. Although the setting is a different (towards marketing and customer targeting), it sheds much light. Social network's dominance in the adoption of innovation is highlighted [START_REF] Kuandykov | Impact of social neighbourhood on diffusion of innovation S-curve[END_REF]. The spread in social networks has received much attention in recent years [START_REF] Jiangs | Diffusion in Social Networks: A Multiagent Perspective[END_REF], [START_REF] Xu | Push-and pull-based epidemic spreading in networks: Threshold and deeper insights[END_REF], [START_REF] Valente | Social network thresholds in the diffusion of innovations[END_REF], [START_REF] Jiang | Concurrent collectives strategy diffusion of multiagents: The spatial model and case study[END_REF]. Actors dissemination (individuals, groups, organizations), a broadcast medium (diffusion environment, strong and weak links between actors, the network structure), and the content to be broadcast are the three elements of the diffusion. This distribution is described as the collective behavior of a group of social actors interacting on social networks [START_REF] Jiang | Understanding social networks from a multiagent perspective[END_REF]. Technology transfer is a kind of diffusion in a social network, which has inspired our model of linkages between actors. In the case of a competition for a market, two types of diffusion models are identified [START_REF] Libai | Decomposing the value of word-of-mouth seeding programs: acceleration versus expansion[END_REF], [START_REF] Kempe | Maximizing the spread of influence through a social network[END_REF]: the threshold models where agents adopt if enough neighbors have, and cascade models where the probability of adoption increases with the number of neighbors who have adopted. In our case, the cascade model was implemented. The closest work to ours is that of Ning and Quing [START_REF] Ning | Influence of information search cost on technology transfer based on multi-agent system[END_REF] which presents a multi agent model for technology transfer. Their model has two kinds of agents (universities and industry), and 4 states, 'doing nothing' to 'active part in a collaboration'. The transfer is modeled between 0 and 100 for each agent. Their results show that the key to a good transfer cost is to seek information (distance between agents) and the probability of finding a partner. The study is limited, omitting financing and turnover to influence direction.

Conclusion

In this paper we analyzed the positive or negative influence of the TTO on the individual objectives of other actors in the technology transfer. We shown that they have a positive role in helping to create contacts between the actors. The ultimate goal of this work is to give a tool to understand the springs of technology transfer in Green IT. Note in passing that the methodology and models developed can be extended to other areas. The next step will be to modify the model so that the actors' behaviors is influenced by their SPI value.

1 :

 1 if not permanent then ttl = ttl -1 2: if ttl ≤ 0 then die 3: e = research facility employing the researcher 4: newpublication = 0 5: for all regular-neighbor do 6: money = funding research of e 7: if random-float 1.0 < (0.20 * 1/90) and money > 1000 then 8: newpublication = newpublication + 1 9: ask e [ funding research = funding research -1000 ] 10: publication = publication + newpublication 11: ask my-regular-links [ if random-float 1 ≤ 1/180 then die ] 12: if count regular-neighbors < max link per researcher then 13: bonus = P(e) 14: if random-float 1 ≤ (1 / 90) + bonus then 15: r = random 100 16:

Fig. 1 .Figure 2

 12 Fig. 1. (a) Mean publications of permanent researchers, as a function of number of TTO (0 to 10) and companies (10, 20 and 50 companies). (b) Mean Reputation of research facilities, as a function of TTO and companies numbers. (left: research facilities without TTO, right: research facilities with TTO)

Fig. 2 .

 2 Fig. 2. (a) SPI value, as a function of TTO and companies numbers. (b) Mean reputation of research facilities, without (red) and with (blue) TTO, as a function of back percentage.