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Game Theoretic Approach to Socially-Aware Energy Management in Cloud Applications

This presentation explores a game-theoretic framework designed to enhance energy efficiency in cloud computing environments. By implementing a decentralized system for load aggregation and the strategic migration of virtual machines (VMs), we aim to significantly reduce energy consumption. Key strategies involve monitoring resource utilization of physical machines (PMs) and dynamically adjusting VM allocations based on energy efficiency thresholds. The proposed methods seek to improve performance while minimizing disruptions to users and sustaining service quality.

jescie-moore
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Game Theoretic Approach to Socially-Aware Energy Management in Cloud Applications

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  1. GameTheoreticapproach to energy efficiency Socially-aware Management of New Overlay Application Traffic combined with Energy Efficiency in the Internet European Seventh Framework STREP FP7-2012-ICT-317846 http://www.smartenit.eu Mateusz Wielgosz, Krzysztof Wajda, AGH KrakowMeeting, June3, 2013

  2. Goal • Reduction of energy consumption via loadaggregation and turningoffphysicalmachines. • Savingsareconsiderable as idleserver(in stand-by mode) canuseup to 66% energy of fully-loadedmachine. • MainstakeholdersherearetheIaaSproviders,potentialinterference forusersshould be keptto a minimum.

  3. Assumptions • We consideronly single resourcetype on PhysicalMachines (PM) and allVirtualMachines (VM) seekthisresource (e.g. processingpower). • VMsare independent. • Cloud hassufficientcapacity to satisfyalldemands. • VM is to be hosted on single PM (VM/demandis not divisible) • Proposedsolutionisdecentralized, but we show howitcould be integratedwithS-Box

  4. Model and game • P = {P1, P2,…, Pj,…, P|P|}, PjcansupplyCj resources. • V = {V1, V2,…, Vi,…, V|V|}, Videmands Di resources. • Lj – totalresourceutilization of Pj. • PM energy consumptionWj= ΣWiDi + Widle • VM „energy efficiency” A(Pj)=Di/Lj • Players: VM Strategy:pick PM Utility:„Energy efficiency”

  5. Procedure • PhysicalMachines (PM): • TrackyourLj (sum currentDi’s) • PrompthousedVMs to check random PM whenever VM leav6es orjoinsthis PM (alt. onlywhenleaves) • (alt) SendLjto S-Box • VirtualMachines (VM): • Initialpick (random by default, alt. recommended by S-Box) • Check random PM whenprompted by PM. (alt. checkS-Box) • Migrate to another PM ifenergy efficiencythereislower by giventhreshold. • Thresholdisused not only to aviodunnecessarymigrations, but also to balancepowersavings and delaycaused by migration.

  6. Chainmigration • Migrationdecreases energy efficiencycostat a local PM and increasesefficiencyattheremote PM. • Di/Lloc < Di/(Lloc- Dx) Di/Lrem > Di/(Lrem+ Dx) • Thereforemigrating VM canleavenew PM addressbehind, for nextVMs. Addressiswipedwhen first VM usesit, otherVMscheck random PM. • Ifremote PM stillhasenough resources it’saddress will be leftbehindagain by second VM. • Ifremote PM is „full”, thenitispossiblethatotherprobingVMs will find PM withlowerpowercost.

  7. Pros and Cons for S-Boxinvolvement • Pros • Better performance • Betterallocationfrom start of theprocedure • Cons • Decentralized • No additionalinfrastructure • No synchronisation

  8. To be adressed • Dynamics – potentialarea for socialawareness. • Migrationcost – powersavingsvs. userdelay.

  9. References • H. Khani, N. Yazdani, S. Mohammadi “Power-AwareGame for Cloud Computing” 6th International Symposium on Telecommunications, 2012. • D. Vesick, D. Tavangarian „Reducing Energy Consumption by LoadAggregationwith an OptimizedDynamic Live Migration of VirtualMachines” 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2010. • A. Gandhi, R. Das, M. Harchol-Balter, C. Lefurgy „Optimal Power Allocationin Server Farms” SIGMETRICS/Performance’09, 2009. • M. Harchol-Balter „Power Management in Data Centers” PROBE 2011.

  10. Thankyou ! Questions?

  11. Backup slides 2

  12. Backup slides 1

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