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Lazaros Gkatzikis – PhD defense – 23/7/2013 Electrical and Computer Engineering, University of Thessaly “Optimization and game theory techniques for energy-

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Presentation on theme: "Lazaros Gkatzikis – PhD defense – 23/7/2013 Electrical and Computer Engineering, University of Thessaly “Optimization and game theory techniques for energy-"— Presentation transcript:

1 Lazaros Gkatzikis – PhD defense – 23/7/2013 Electrical and Computer Engineering, University of Thessaly “Optimization and game theory techniques for energy- constrained networked systems and the smart grid” Lazaros Gkatzikis Dissertation Committee: Leandros Tassiulas (UTH,GR) Iordanis Koutsopoulos (AUEB, GR) Slawomir Stanczak (TUB, GER) 1

2 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Energy efficiency of the power grid ◦ Residential demand response ◦ Hierarchical demand response markets 3) Mobile task offloading in the cloud 4) Energy-efficient wireless communications ◦ Energy-constrained MAC ◦ Interference-aware relay selection and power control 5) Conclusion 2

3 Lazaros Gkatzikis – PhD defense – 23/7/2013 Thesis Summary 3

4 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Residential demand response 3) Hierarchical demand response markets 4) Mobile task offloading in the cloud 5) Energy-constrained MAC 6) Interference-aware relay selection and power control 7) Conclusion 4

5 Lazaros Gkatzikis – PhD defense – 23/7/2013 Energy consumption Annual worldwide demand for electricity increased ◦ ten-fold within the last 50 years ◦ almost doubled in the last decade 5 Cost of non-renewable sources constantly increasing Electricity prices follow

6 Lazaros Gkatzikis – PhD defense – 23/7/2013 Pursue energy efficiency optimizing the efficiency and reliability of the power grid improving efficiency of individual devices or systems 6 Smart Grid Demand response ◦ Time-varying prices to reduce demand in peak periods ◦ Users: lower electricity bill ◦ Utility: lower generation cost Networked systems ICT = 5% of worldwide electricity consumption Major consumers ◦ Datacenters (cloud) ◦ Wireless access (WiFi, 4G) Energy-constrained mobiles Battery- powered Contention for resources

7 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Residential demand response 3) Hierarchical demand response markets 4) Mobile task offloading in the cloud 5) Energy-constrained MAC 6) Interference-aware relay selection and power control 7) Conclusion 7

8 Lazaros Gkatzikis – PhD defense – 23/7/2013 Flat pricing ◦ Same price throughout the day ◦ Users schedule demands for most convenient time Result: unbalanced demand Balanced demand guarantees ◦ stability of electricity network ◦ reduced generation cost (convex function of demand) ◦ reduced electricity bill (user side) Solution: Dynamic pricing (day-ahead DR market) ◦ enabled by smart meters ◦ motivates demand shifting Balancing demand through dynamic pricing

9 Lazaros Gkatzikis – PhD defense – 23/7/2013 Negotiation phase (repeated until convergence) ◦ operator updates prices ◦ individuals respond (automated) Wholesale auction ◦ auction to meet demand: generators make energy/price bids ◦ all accepted offers paid at market clearing price Real time spot market ◦ whenever actual demand exceeds prediction ◦ usually at a higher cost Day-Ahead Market 9

10 Lazaros Gkatzikis – PhD defense – 23/7/2013 Convenient model of splittable demands ◦ Operator: maximize social welfare price=marginal cost ◦ End-users: for given prices p maximize utility Control : x the daily electricity consumption vector However, most appliances have a specific consumption pattern Our contribution ◦ Devise a realistic DR market model ◦ Quantify DR benefits for each entity through realistic traces Related Work and Contribution PaymentComfort 10

11 Lazaros Gkatzikis – PhD defense – 23/7/2013  User objective: for given day ahead prices find the optimal shift  Control δ : time shift vector (separable per appliance) A realistic model for residential DR Payment Comfort  For each demand  arrival/end time (a,e)  consumption requirement (w )  deadline(d )  elasticity parameter θ Feasible shifts

12 Lazaros Gkatzikis – PhD defense – 23/7/2013  Operator objective: minimize electricity generation cost ◦ cost is a convex function of the total demand χ t within a timeslot ◦ constraint guarantees that average price is at most equal to flat price (attract users to enroll)  Result: Even when operator has direct control over demands, optimal scheduling is NP-Hard  Additional challenge: elasticity θ is user’s private information  Negotiation-based iterative approach ◦ use total demand as the gradient ◦ increase price at peak consumption periods ◦ reduce price at low demand periods Price setting strategy 12

13 Lazaros Gkatzikis – PhD defense – 23/7/2013 Numerical Results Significantly better than the proportional scheme Similar to lower bound Proposed pricing Demand-proportional pricing scheme [19] A lower bound of the generation cost 13

14 Lazaros Gkatzikis – PhD defense – 23/7/2013 Numerical Results and Conclusion 14 Significantly reduced cost even for low elasticity Highly inelastic users experience increased prices and hence reduced utility Conclusion: Existing models overestimate cost benefits of residential DR Future Work: Devise regression based methods that accurately estimate the utility function of a user through historical data

15 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Residential demand response 3) Hierarchical demand response markets 4) Mobile task offloading in the cloud 5) Energy-constrained MAC 6) Interference-aware relay selection and power control 7) Conclusion 15

16 Lazaros Gkatzikis – PhD defense – 23/7/2013 Aggregators as DR enablers An intermediate is required, due to: ◦ the large number of home users (scalability) ◦ the utility operator lacks DR knowhow ◦ each home controls tiny demand limited negotiation power Aggregator ◦ installation of the smart meters at homes ◦ compensate users to shift demands ◦ resell DR services to the operator Operator: Rewards aggregators for their DR services Users: Adjust demand to dynamic prices Objective: Investigate share of DR benefits among market entities 16

17 Lazaros Gkatzikis – PhD defense – 23/7/2013 Related Work and Contribution The role of aggregators has received limited research attention ◦ Single timeslot models where operator sets the demand target ◦ Aggregator has no incentive to participate Commercial programs ◦ Direct load control in emergency events ◦ Fixed monthly compensation to contracted end-users (mainly industrial) Main issue: Utilities reap DR benefits, users have to invest Contribution ◦ Formulate objectives of utility operator, several competing aggregators and home users ◦ Investigate impact of operator’s reward strategy on DR gains ◦ Devise a day-ahead DR market scheme that guarantees a fair fraction of DR benefits for each entity 17

18 Lazaros Gkatzikis – PhD defense – 23/7/2013 Day-ahead market ◦ T timeslots Utility operator: minimize operating cost ◦ electricity generation + reward J competing aggregators: maximize net profit ◦ reward - compensation N home users: maximize net utility ◦ compensation - discomfort ◦ discomfort function ◦ v i inelasticity parameter of user i ◦ Each user is tied to an aggregator Hierarchical DR market model 18

19 Lazaros Gkatzikis – PhD defense – 23/7/2013 Operator: min operating cost controls reward generation cost c t (): convex and increasing function of total demand y t DR gain portion of DR gains provided as reward Hierarchical DR market model Aggregator j: max reward - compensation indirectly controls its users’ demand distribution d jt through time-varying compensation p jt reward depends also on demand / strategy of other aggregators 19

20 Lazaros Gkatzikis – PhD defense – 23/7/2013 Hierarchical DR Market Model User: max compensation – discomfort ◦ controls demand distribution x i ◦ total demand is fixed (only demand shifting) ◦ for given compensation p jt, a convex optimization problem Three level DR market

21 Lazaros Gkatzikis – PhD defense – 23/7/2013 Proposed market mechanism Day-ahead DR market 1.Operator announces a reward per unit of cost reduction. 2.Each aggregator bids the cost reduction it can provide for the given reward. 3.The best offer is accepted. 4.An increased reward is announced to achieve further DR gains. 5.Repeat until no further DR benefits In order to calculate their bid, aggregators estimate inelasticity of users 21

22 Lazaros Gkatzikis – PhD defense – 23/7/2013 Numerical results Generation cost is decreasing in reward λ Operating cost is not monotonic DR gain and rewards of lower levels are increasing in λ 22

23 Lazaros Gkatzikis – PhD defense – 23/7/2013 Numerical Results and Conclusions Elasticity of demands is beneficial for DR Users’ utility is not monotonic Non-profit utility operators maximize DR benefits Future work: coalition formation of home users (virtual aggregators) 23

24 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Residential demand response 3) Hierarchical demand response markets 4) Mobile task offloading in the cloud 5) Energy-constrained MAC 6) Interference-aware relay selection and power control 7) Conclusion 24

25 Lazaros Gkatzikis – PhD defense – 23/7/2013 Virtualization: multiple VMs on a single physical machine Multitenancy cost due to access to the same physical resources (CPU, caches, memory, disks, I/O) ◦ generally increasing in the number of co-located VMs ◦ task-dependent Objective: minimize execution time Control: Allocation and migrations of VMs ◦ Migration cost = data transfer time required for initialization of a new VM Mobiles of limited energy offload tasks to the cloud Novel MCC architecture ◦ servers in wireless access hubs ◦ avoid communication delay of the Internet 25 Mobile Cloud Computing

26 Lazaros Gkatzikis – PhD defense – 23/7/2013 Related Work and Contribution Modeling multitenancy cost ◦ profiling of each type of task (BUT significantly diverse tasks exist in the cloud) ◦ estimate probability of contention for each resource (BUT requires a priori knowledge of the resource access pattern of each task) Commercial cloud services (Amazon EC2, Windows Azure) ◦ only availability SLAs (99,9%) ◦ no QoS guarantees Main issue: Dynamic + unpredictable environment Solution: Exploit VM migrations to reconfigure the cloud Our Contribution ◦ Propose three online VM migration mechanisms that capture multitenancy and migration costs ◦ Demonstrate how VM migrations can be used for energy efficiency purposes. 26

27 Lazaros Gkatzikis – PhD defense – 23/7/2013 A task of increasing data footprint No-migration: worst performance Join the least loaded server ◦ attempts to exploit available processing capacity, ◦ does not consider the increasing cost of each migration and the additional cost of downloading the final data from a distant server Mobility-aware: minimizes total lifetime (considers both DL time and migration cost)=> minimizes both execution and DL time 27 Motivating example

28 Lazaros Gkatzikis – PhD defense – 23/7/2013 demand varies unpredictably with time and location ◦ new tasks arise continually at various locations ◦ others complete service same holds for the resource supply due to multitenancy ◦ The available processing capacity changes due to the unpredictable interaction of co-located VMs tasks carry/generate data whose volume varies with time ◦ Video compression: decreasing data footprint ◦ Scientific experiments: increasing data access links are also time varying 28 Challenges

29 Lazaros Gkatzikis – PhD defense – 23/7/2013 tasks arrive continually with unknown distribution at time t each task i is characterized by d i (t): data footprint evolution b i (t): remaining processing requirement  at server j hosting n VMs actual service capacity ◦ stochastic due to multitenancy cost ε 29 System model

30 Lazaros Gkatzikis – PhD defense – 23/7/2013 Online task migration mechanisms triggered ◦ periodically (every τ seconds) or ◦ by load-imbalance signal or ◦ by SLA-violation key idea: migrate only if beneficial for the total processing time, including both migration cost and download time online estimation of multitenancy ◦ online measurements as tasks are being executed 30 Balancing the cloud through migrations

31 Lazaros Gkatzikis – PhD defense – 23/7/2013 cloud facility operated by a single provider A migration affects the tasks running at the current and the destination server consider only migrations beneficial for the system as a whole Strategy : For each task hosted in each server, calculate the gain of migrating to any other server prefer migrating tasks ◦ of increasing data footprint, ◦ of significant remaining processing burden, ◦ introducing significant multitenancy cost (noisy neighbours) 31 Cloud-wide migration

32 Lazaros Gkatzikis – PhD defense – 23/7/2013 Strategy : each server individually selects its migration strategy select the task of maximum anticipated gain, in terms of completion time (does not consider the impact of the migration on the tasks located at the destination host) executed whenever a server detects that it is overloaded compared to the average of the system Application scenario: Intercloud ◦ several providers form coalitions, enabling access to each other’s infrastructure ◦ Reduce the deployment costs ◦ Efficient utilization of resources 32 Server initiated task migration

33 Lazaros Gkatzikis – PhD defense – 23/7/2013 Each user decides independently his migration strategy towards minimizing his own completion time. Challenge: the users of a cloud facility do not have a detailed view of the system ◦ Only aware of advertised processing capacities Strategy: myopically select a migration to the best destination server Application scenario: each task/user may select out of a set of available cloud-providers/ servers. 33 Task initiated migration

34 Lazaros Gkatzikis – PhD defense – 23/7/2013 Numerical results 34 significant benefits compared to one-shot placement performance degrades as we move from the centralized approach (system–wide information) to decentralized ones (local information) migrations crucial for overcommitted clouds or light tasks

35 Lazaros Gkatzikis – PhD defense – 23/7/2013 Energy Concerns and Conclusions Mobile: when is offloading beneficial? ◦ Energy cost of transmitting required data is less than that of local execution 35 Cloud provider: How could electricity cost be reduced through migrations? ◦ Consolidation into minimum number of servers ◦ Exploitation of spatiotemporal variation of prices Conclusions Minimizing energy consumption and execution time are contradictory objectives Multitenancy-aware VM migration schemes necessary for overcommitted clouds Future work: energy-driven VM migration schemes with QoS

36 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Residential demand response 3) Hierarchical demand response markets 4) Mobile task offloading in the cloud 5) Energy-constrained MAC 6) Interference-aware relay selection and power control 7) Conclusion 36

37 Lazaros Gkatzikis – PhD defense – 23/7/2013 Energy efficiency wireless mobile devices are battery powered (i.e. tight energy budget) energy is consumed at electronic compartments (e.g. local oscillator), even when idle Bandwidth scarcity Limited bandwidth for an ever-increasing number of wireless devices Result: extreme competition for the medium Additional constraints autonomous nature of mobile terminals limited knowledge available at node level difficulty of synchronization Need for: Game theoretic models for energy-constrained probabilistic medium access Energy Constraints and Medium Access 37

38 Lazaros Gkatzikis – PhD defense – 23/7/2013 Related work and Contribution Cut down unnecessary energy costs Turn-off electronic parts if not used Support low power (sleep) modes Switching time / consumption tradeoff Mode transition not feasible at timeslot timescale Related Work game theoretic formulation of probabilistic medium access interplay of medium access contention and energy consumption Contribution Derive throughput optimal and proportional fair probabilistic strategies under energy constraints Quantify the impact of energy constraints on probabilistic medium access 38

39 Lazaros Gkatzikis – PhD defense – 23/7/2013 Single channel Aloha network (slotted) N throughput maximizing self-interested users of energy budget Two power modes: ON/OFF We introduce a new timescale (frame) for the mode switching Probabilistic ON/OFF with q i being the ON probability Within a frame the ON nodes access the medium probabilistically (p i ) System Model 39

40 Lazaros Gkatzikis – PhD defense – 23/7/2013 The impact of energy constraints on system throughput Derive throughput optimal p,q (coordinated) to quantify the impact of energy constraints to serve as a benchmark where and 40

41 Lazaros Gkatzikis – PhD defense – 23/7/2013 Coordinated approaches Throughput optimal probabilistic access without energy constraints ◦ Any strategy that eliminates contention Throughput optimal for our case ◦ Activate the j less constrained terminals with ◦ The optimal strategy is of the form Proportional fairness ◦ substitute the original objective by ◦ The optimal strategy is of the form 41

42 Lazaros Gkatzikis – PhD defense – 23/7/2013 Game theoretic model Players: the N users Strategies: the ON-OFF probability and the medium access probability User preferences: any user prefers the strategy that maximizes his throughput Optimal strategy Unique Nash Equilibrium Point (NEP) Bounded price of anarchy ◦ In contrast to the classic Aloha games 42

43 Lazaros Gkatzikis – PhD defense – 23/7/2013 A modified (sensing) strategy Assumption: fixed medium access probability within a frame two or more users are ON within a frame zero payoff Ideally: whenever a collision is detected in the first timeslot, all but one should backoff until the next frame Save energy Reduce contention Our approach: If the transmission fails the user adjusts his strategy by reducing his transmission probability to Derive analytic expressions of throughput and energy Formulation of a non-cooperative game: Multiple NEPs 43 Assumption: fixed medium access probability within a frame

44 Lazaros Gkatzikis – PhD defense – 23/7/2013 Numerical Evaluation the additional power budget, increases the performance degradation due to the additional collisions caused Performance plateau (a single user has sufficient energy to capture the medium on its own) 44

45 Lazaros Gkatzikis – PhD defense – 23/7/2013 Numerical Evaluation and Conclusions High transmission cost makes the users less aggressive, leading thus to reduced collisions Conclusions Channel sensing provides significant benefits Due to lack of coordination, probabilistic access is sensitive to increased energy availability Future work: Mechanism design for more efficient equilibria 45

46 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Residential demand response 3) Hierarchical demand response markets 4) Mobile task offloading in the cloud 5) Energy-constrained MAC 6) Interference-aware relay selection and power control 7) Conclusion 46

47 Lazaros Gkatzikis – PhD defense – 23/7/2013 Cooperative communications exploit the broadcast nature of the wireless medium use intermediate nodes as relays Multihop communications - require additional radio resources (frequency channels or time slots) + reduce the path loss, by shortening the propagation path + create diverse paths that mitigate the effects of fading Upcoming 4G cellular networks use relays to extend coverage enhance throughput minimum deployment cost 47 Relay-assisted networks SD R

48 Lazaros Gkatzikis – PhD defense – 23/7/2013 Most existing works assume that the transmissions take place over orthogonal channels consider interference either negligible or handle it as noise In practice Channel scarcityfrequency reuse Maximizing the sum rate of a system N source-destination unicast pairs single channel (interference) contention for K relays Controls: Relay Selection+ Power Control Our contribution Develop distributed lightweight resource allocation algorithms Derive conditions for the optimality 48 Related Work and Contribution

49 Lazaros Gkatzikis – PhD defense – 23/7/2013 Relay selection Objective: maximizing the sum rate of the system where Decode and Forward scenario (half-duplex) Challenges due to interference relay selection and power control strongly coupled one’s transmission power affects all the others first and second hop transmission rates are coupled 49 Problem Formulation

50 Lazaros Gkatzikis – PhD defense – 23/7/2013 The optimality of a relay assignment depends on the selected transmission powers and vice versa joint RS and PC extremely difficult decouple by solving the two problems in an iterative way initial transmission power allocation Logical assumption: Given the others’ powers, the rate of a node is an increasing function of its transmission power 50 Decoupling Relay selection & Power control

51 Lazaros Gkatzikis – PhD defense – 23/7/2013 Bipartite Maximum Weighted Matching (MWM) approach map into the problem of finding the maximum weighted matching in a properly constructed complete bipartite graph introduce virtual nodes interference in the receiver of each link needs to be known (true only for the 1 st hop) conservative approach: ◦ assume that in the optimal assignment all the relays are used ◦ overestimate interference 51 Relay selection

52 Lazaros Gkatzikis – PhD defense – 23/7/2013 Given a relay assignment, we need to find the optimal transmission powers for the sources and the relays Rate equalization (Req) approach ◦ coupled power control in the 1 st and the 2 nd hop ◦ for each communication pair we reduce the transmission power to match the rate of the other hop (until convergence) ◦ Guarantees reduced interference and energy consumption Joint source and relay power control (JsrPC) approach ◦ we extend for the two hop scenario the approach of [90] that is based on the high SINR approximation 52 Power control

53 Lazaros Gkatzikis – PhD defense – 23/7/2013 Benchmark Direct Transmission one-shot greedy approach: Each source greedily selects the best relay Impact of number of relays K Dense network : interference overestimation degrades performance Sparse network: performance beyond 15 relays is less affected Conclusion: Significant benefits from interference management and relaying (main features of 4G) for both energy and throughput. 53 Simulation Results and Conclusion

54 Lazaros Gkatzikis – PhD defense – 23/7/2013 Outline 1) Introduction 2) Residential demand response 3) Hierarchical demand response markets 4) Mobile task offloading in the cloud 5) Energy-constrained MAC 6) Interference-aware relay selection and power control 7) Conclusion 54

55 Lazaros Gkatzikis – PhD defense – 23/7/2013 Conclusion Explored the challenges arising from limited availability of energy at device, system and community level ◦ Energy constraints modify medium access (different NEPs, reduced PoA) ◦ Relay selection and power control lead to better utilization of available energy (interference management) ◦ VM migrations enable better exploitation of cloud resources, reduce energy consumption and operating cost ◦ DR leads to less costly and more stable power grid and can be beneficial for all market entities 55

56 Lazaros Gkatzikis – PhD defense – 23/7/2013 Related Publications Journal publications [J.01] L. Gkatzikis, I. Koutsopoulos and T. Salonidis, “The Role of Aggregators in Smart Grid Demand Response Markets,” in IEEE JSAC- Special series on Smart Grid Communications, vol.31, no.7, pp 1247 - 1257, July 2013. [J.02] L. Gkatzikis and I. Koutsopoulos, “Migrate or Not? Exploiting Dynamic Task Migration in Mobile Cloud Computing Systems,” in IEEE Wireless Communications Magazine: Special Issue on Mobile cloud computing vol.20, no.3, June 2013. [J.03] L. Gkatzikis, and I. Koutsopoulos, “Mobiles on Cloud Nine: Efficient Task Migration Policies for Cloud Computing Systems,” under review in Elsevier Computer Networks, Special Issue on Communications and Networking in the Cloud. [J.04] L. Gkatzikis, T. Salonidis, N. Hegde and L. Massoulie, “The Impact of Shiftable Demands on Residential Demand Response,” to be submitted in IEEE Transactions in Smart Grid. Conference publications [C.01] L. Gkatzikis, G. Iosifidis, I. Koutsopoulos and L. Tassiulas, “Collaborative Placement and Use of Storage Resources in the Smart Grid,” under review in IEEE International Conference on Smart Grid Communications (SmartGridComm), 2013. [C.02] L. Gkatzikis, G.S. Paschos and I. Koutsopoulos, “Medium Access Games: The impact of energy constraints,” in proc. of International Conference on Network Games, Control and Optimization (NETGCOOP), 2011. [C.03] L. Gkatzikis and I. Koutsopoulos, “Low Complexity Algorithms for Relay Selection and Power Control in Interference-Limited Environments,” in proc. of Intl. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2010, Avignon, France 56

57 Lazaros Gkatzikis – PhD defense – 23/7/2013 Not Directly Related Publications Journal publications [J.05] Vasilis Sourlas, Lazaros Gkatzikis, Paris Flegkas and Leandros Tassiulas, “Autonomic Cache Management and Performance Limits in Information-Centric Networks,” to appear in IEEE Transaction on Network and Service Management (TNSM), 2013. [J.06] I. Koutsopoulos, L. Tassiulas and L. Gkatzikis, “Client and Server Games and Nash Equilibria in Peer-to-Peer Networks,” under review in Elsevier Computer Networks. Conference publications [C.04] P. Mannersalo, G.S. Paschos and L. Gkatzikis, “Geometrical Bounds on the Efficiency of Wireless Network Coding,” to appear in proc. of Intl. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2013. [C.05] L. Gkatzikis, T. Tryfonopoulos and I. Koutsopoulos, “An Efficient Probing Mechanism for Next Generation Mobile Broadband Systems,” in proc. of IEEEWireless Communications and Networking Conference (WCNC), Paris, France, 2012. [C.06] Vasilis Sourlas, Paris Flegkas, Lazaros Gkatzikis and Leandros Tassiulas, “Autonomic Cache Management in Information-Centric Networks,” in 13th IEEE/IFIP Network Operations and Management Symposium (NOMS 2012), pp. 121-129, Hawaii, USA, April 2012. [C.07] L. Gkatzikis, T. Salonidis, N. Hegde and L. Massoulie, “Electricity Markets Meet the Home through Demand Response,” in proc. of IEEE Conference on Decision and Control (CDC), Maui, Hawai 2012. [C.08] Vasilis Sourlas, Lazaros Gkatzikis and Leandros Tassiulas, “On-Line Storage Management with Distributed Decision Making for Content-Centric Networks,” in 7th Conference on Next Generation Internet (NGI) 2011, pp. 1-8, Kaiseslautern, Germany, June 2011. [C.09] I. Koutsopoulos, L. Tassiulas and L. Gkatzikis, “Client and Server Games in Peer-to-Peer Networks,” in proc. of IEEE International Workshop on QoS (IWQoS), 2009, Charleston, SC, USA. 57

58 Lazaros Gkatzikis – PhD defense – 23/7/2013 Thank you!!! 58


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