Electrical and Computer Engineering, University of Thessaly

1. Electrical and Computer Engineering, University of Thessaly “Optimization and game theory techniques for energy-constrained networked systems and the smart grid” LazarosGkatzikis Dissertation Committee: Leandros Tassiulas (UTH,GR) IordanisKoutsopoulos (AUEB, GR) SlawomirStanczak(TUB, GER)

2. 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

3. Thesis Summary

4. 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

5. Energy consumption • Annual worldwide demand for electricity increased • ten-fold within the last 50 years • almost doubled in the last decade • Cost of non-renewable sources constantly increasing • Electricity prices follow

6. Pursue energy efficiency • optimizing the efficiency and reliability of the power grid • improving efficiency of individual devices or systems 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. 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 andpower control 7) Conclusion

8. Balancing demand through dynamic pricing • 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

9. Day-Ahead Market • 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

10. Related Work and Contribution • Convenient model of splittable demands • Operator: maximize social welfare price=marginal cost • End-users: for given prices pmaximize utility Control : x thedaily 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 Comfort Payment

11. A realistic model for residential DR • For each demand • arrival/end time (a,e) • consumption requirement (w ) • deadline(d ) • elasticity parameterθ • User objective: for given day ahead prices find the optimal shift • Control δ:time shift vector (separable per appliance) Payment Comfort Feasible shifts

12. Price setting strategy • Operator objective: minimize electricity generation cost • cost is a convex function of the total demand χt 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

13. Numerical Results • Demand-proportional pricing scheme [19] • A lower bound of the generation cost • Significantly better than the proportional scheme • Similar to lower bound Proposed pricing

14. Numerical Results and Conclusion 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. 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

16. 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

17. 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

18. Hierarchical DR market model • 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 • vi inelasticity parameter of user i • Each user is tied to an aggregator

19. Hierarchical DR market model Operator: min operating cost • controls reward • generation costct(): convex and increasing function of total demand yt • DR gain • portion of DR gains provided as reward Aggregator j: max reward - compensation • indirectly controls its users’ demand distribution djt through time-varying compensation pjt • reward depends also on demand / strategy of other aggregators

20. Hierarchical DR Market Model • User: max compensation – discomfort • controls demand distribution xi • total demand is fixed (only demand shifting) • for given compensation pjt , a convex optimization problem • Three level DR market

21. Proposed market mechanism • Day-ahead DR market • Operator announces a reward per unit of cost reduction. • Each aggregator bids the cost reduction it can provide for the given reward. • The best offer is accepted. • An increased reward is announced to achieve further DR gains. • Repeat until no further DR benefits • In order to calculate their bid, aggregators estimate inelasticity of users

22. 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

23. 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)

24. 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

25. Mobile Cloud Computing • 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

26. Related Work and Contribution • Modeling multitenancycost • 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 QoSguarantees • 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.

27. Motivating example • 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

28. Challenges • 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

29. System model • tasks arrive continually with unknown distribution • at time t each task i is characterized by di(t):data footprint evolution bi(t): remaining processing requirement • at server j hosting nVMsactual service capacity • stochastic due to multitenancycostε

30. Balancing the cloud through migrations • 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

31. Cloud-wide migration • 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)

32. Server initiated task migration 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

33. Task initiated migration • 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.

34. Numerical results • 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. Energy Concerns and Conclusions • Mobile: when is offloading beneficial? • Energy cost of transmitting required data is less than that of local execution • 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. 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

37. Energy Constraints and Medium Access 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

38. 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

39. System Model • 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 qi being the ON probability • Within a frame the ON nodes access the medium probabilistically (pi)

40. 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

41. 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

42. 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

43. A modified (sensing) strategy • Assumption: fixed medium access probability within a frame • 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

44. 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)

45. 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

46. 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

47. Relay-assisted networks 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 R S D

48. Related Work and Contribution 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

49. Problem Formulation 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

50. Decoupling Relay selection & Power control • 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