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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)

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Outline1) Introduction2) 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

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Thesis Summary

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Outline

1) Introduction2) Residential demand response3) Hierarchical demand response markets4) Mobile task offloading in the cloud 5) Energy-constrained MAC6) Interference-aware relay selection and

power control7) Conclusion

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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

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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 consumptionMajor consumers

◦ Datacenters (cloud)◦ Wireless access (WiFi, 4G)

Energy-constrained mobiles Battery- powered Contention for resources

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Outline

1) Introduction2) Residential demand response3) Hierarchical demand response markets4) Mobile task offloading in the cloud 5) Energy-constrained MAC6) Interference-aware relay selection and

power control7) Conclusion

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Flat pricing◦ Same price throughout the day◦ Users schedule demands for

most convenient timeResult: 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

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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

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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

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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

PaymentComfort

For each demand arrival/end time

(a,e) consumption

requirement (w )

deadline(d ) elasticity

parameterθ

Feasible shifts

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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

Price setting strategy

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013Numerical Results

Significantly better than the proportional schemeSimilar to lower bound

Proposed pricing

Demand-proportional pricing scheme [19] A lower bound of the generation cost

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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 DRFuture Work: Devise regression based methods that accurately estimate the utility function of a user through historical data

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Outline

1) Introduction2) Residential demand response3) Hierarchical demand response markets4) Mobile task offloading in the cloud 5) Energy-constrained MAC6) Interference-aware relay selection and

power control7) Conclusion

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013Aggregators 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 entities16

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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

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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

Hierarchical DR market model

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Operator: min operating cost controls reward generation cost ct(): convex and

increasing function of total demand yt

DR gain

portion of DR gains provided as reward

Hierarchical DR market model

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

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013Hierarchical 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

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Proposed market mechanismDay-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

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Numerical results

Generation cost is decreasing in reward λ Operating cost is not monotonicDR gain and rewards of lower levels are increasing in

λ

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Numerical Results and Conclusions

Elasticity of demands is beneficial for DRUsers’ utility is not monotonicNon-profit utility operators maximize DR benefitsFuture work: coalition formation of home users

(virtual aggregators)

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Outline

1) Introduction2) Residential demand response3) Hierarchical demand response markets

4) Mobile task offloading in the cloud 5) Energy-constrained MAC6) Interference-aware relay selection and

power control7) Conclusion

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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

Mobile Cloud Computing

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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.

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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

Motivating example

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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

Challenges

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tasks arrive continually with unknown distribution

at time t each task i is characterized bydi(t): data footprint evolution

bi(t): remaining processing requirement

at server j hosting n VMs actual service capacity

◦ stochastic due to multitenancy cost ε

System model

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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

Balancing the cloud through migrations

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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)

Cloud-wide migration

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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

Server initiated task migration

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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.

Task initiated migration

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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

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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 pricesConclusions 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

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Outline

1) Introduction2) Residential demand response3) Hierarchical demand response markets4) Mobile task offloading in the cloud 5) Energy-constrained MAC6) Interference-aware relay selection and

power control7) Conclusion

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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

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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

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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)

System Model

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013The 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

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013Coordinated 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

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013Game theoretic model

Players: the N usersStrategies: the ON-OFF probability and the medium access probabilityUser 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

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013A 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 frameSave energyReduce 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

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Assumption: fixed medium access probability within a frame

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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)

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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 availabilityFuture work: Mechanism design for more efficient equilibria

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Outline

1) Introduction2) Residential demand response3) Hierarchical demand response markets4) Mobile task offloading in the cloud 5) Energy-constrained MAC6) Interference-aware relay selection and

power control7) Conclusion

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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

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Relay-assisted networks

S DR

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Most existing works assume that the transmissions take place over orthogonal channels consider interference either negligible or handle it as noise

In practice Channel scarcity frequency reuse

Maximizing the sum rate of a system N source-destination unicast pairs single channel (interference) contention for K relaysControls: Relay Selection+ Power Control

Our contribution Develop distributed lightweight

resource allocation algorithms Derive conditions for the optimality

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Related Work and Contribution

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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

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Problem Formulation

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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

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Decoupling Relay selection & Power control

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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 1st hop) conservative approach:

◦ assume that in the optimal assignment all the relays are used

◦ overestimate interference

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Relay selection

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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 1st and the 2nd 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

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Power control

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Benchmark Direct Transmission one-shot greedy approach: Each source greedily selects the best relayImpact of number of relays K Dense network : interference overestimation degrades performance Sparse network: performance beyond 15 relays is less affectedConclusion: Significant benefits from interference management and relaying (main features of 4G) for both energy and throughput.

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Simulation Results and Conclusion

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Outline

1) Introduction2) Residential demand response3) Hierarchical demand response markets4) Mobile task offloading in the cloud 5) Energy-constrained MAC6) Interference-aware relay selection and

power control7) Conclusion

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ConclusionExplored 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

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Related PublicationsJournal 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

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Not Directly Related PublicationsJournal 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.

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Thank you!!!