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Energy-Efficient Resource Utilization in Cloud Computing Systems

Explore the on-line algorithms for energy-efficient cloud resource utilization. Learn about the Elevator or Stairs problem, Bin Packing, power-saving modes, dynamic scaling techniques, and the Energy Cloud Model. Discover the ECTC and MaxUtil algorithms for optimizing resource consolidation density and energy consumption. Read about experimental evaluation results and different resource usage patterns. Conclude with the importance of strict modeling and the need for sophisticated algorithms in this critical domain.

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Energy-Efficient Resource Utilization in Cloud Computing Systems

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  1. 1. On-Line Algorithms2. Energy efficient utilization of resources in cloud Raziel Hess-Green

  2. On-Line Algorithms A small intro Raziel Hess-Green

  3. Elevator or Stairs problem • More known as: “ski-rental problem” • Stairs: takes time S • Elevator: takes time L<S • The ultimate question: • How long to wait?

  4. Evaluate on-line algorithms • Competitive ratio – Alg/OPT • worst case over all possible events • Alg = cost of algorithm • OPT = optimal cost in hindsight.

  5. Back to elevators and stairs • Wait until elevator comes • What if it’s broken? • Take stairs immediately • Bad competitive ratio - S/L

  6. 2-competitive • Wait until you should have taken the stairs, then take the stairs • Case 1: • Elevator comes before time S-L: optimal. • Case 2: • Elevator comes after: you paid 2S-L, OPT paid S. Ratio = 2 - L/S.

  7. That’s the best possible • Elevator arrives right after you give up: • If you wait longer, numerator goes up but the denominator stays the same, so your ratio is worse. • If you wait less, then the numerator and the denominator go down by the same amount, worse.

  8. Bin Packing • BP: • Given N items with sizes s1, s2,…, sN, where0si 1. The bin packing is to pack these items in the fewest bins, given that each bin has unit capacity. • On-line bin packing: • Each item must be placed in a bin before the size of the next item is given. • Stay tuned for more..

  9. Energy efficient utilization of resources in cloudcomputing systems Young ChoonLee, Albert Y. Zomaya

  10. Elictricity in Data Centers • 2000 – 2005 • Doubled! • 2005 cost 7.2 bnUS$ • 2005-2010 • Predicted by the EPA at 2007 to double again • Actually added around 56% (J. Koomey) • Mainly due to 2008 recession • 2011 • 2% of USA electricity

  11. With Great Power Comes Huge: Electricity Bill

  12. UtilityComputing • Cloud Computing allows for fuller utilization of hardware • Energy consumption is turning into a major issue • Costly • CO2 emission • Must hold enough resources to handle peak demand • Energy grows linearly with utilization

  13. Turn Off Power? • 20% utilization • Idle servers can use 60% of full utilization • Turning off is problematic • Long turn on time • May increase failure rate

  14. Power Saving Mode • Must have the server totally unutilized to enable sleep mode • Dynamic Voltage and Frequency Scaling (DVFS) • Intel SpeedStep • AMD PowerNow! • Started in laptops and mobile devices • Now used in servers • Much more research on this: • PowerNap (ASPLOS ’09)

  15. Model • Cloud • Application • Energy

  16. Cloud Model • Resources • set R of r resources/processors fully interconnected • Homogeneous • Communication • Same DC • Live Migration

  17. Application Model • IaaS, SaaS or PaaS • regarded as tasks • Assumed: known time and CPU demand • IaaS has predefined time/CPU requirements • For SaaS and PaaS- obtain estimates from history and/or from consumer

  18. Energy Model • linear relationshipwith processing time and utilization: • - utilization of task on • Energy during Power Save mode:

  19. Task consolidation problem • Assigning a set N of n tasksto a set R of r cloud resources • Maximize resource utilization • In order to minimize energy consumption • By enabling resources to sleep • Without violating constraints • time • Usage • Hard constraints

  20. The Algorithms • Two algorithms presented, differ only in cost function • ECTC • Explicitly computes energy consumption • MaxUtil • Average utilization -during processing time of the task to schedule • Increase consolidation density

  21. The Algorithm:

  22. ECTC • τ0 – ((τ1 +.τ2) • - utilization rate of the task • - total processing time of the task • τ1-time task will run alone • τ2- time task will run in parallel

  23. MaxUtil • Maximize average consolidation density • Over all processing time of task j

  24. Example ECTC

  25. Example MaxUtil

  26. Experimental evaluation • Random • ECTC • MaxUtil • 1,500 experiments • 50 different number of tasks • 100-5,000 with intervals of 100 • 10 mean inter-arrival times (10 -100) • Poisson process

  27. Usage patterns • Three usage patterns • Random • Uniformly distributed between 0.1 and 1 • Low • Gaussian, mean utilization rates of 0.3 • High • Gaussian, mean utilization rates of 0.7

  28. Task processing time • Exponential distribution • Assume: 300-200 watt active mode consumption • _m • Adding migration

  29. Results • Relative energy savings • MaxUtil • ECTC • Different resource usage patterns • Low • High • Random

  30. MaxUtil and ECTC vs Random

  31. Low resource usage

  32. High resource usage

  33. Random resource usage

  34. Ending Remarks • Important problem • Strict modeling • All demands known exactly (time, usage) • Communication is “free” • And yet: No sophisticated algorithms • No “make sense” for results • No comparing to previous work • “existing task consolidation algorithms are not directly comparable to our heuristics”

  35. SBP David Breitgand, Amir Epstein (IBM, Haifa) • Stochastic Bin Packing (SBP)problem • each virtual machine's bandwidth demand is treated as a random variable. • both offline and online versions are treated • assumption: VMs' bandwidth consumption obeys normal distribution • show a 2-approximation algorithm for the offline version • (2+Ɛ)-competitive algorithm for online version

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