Optimization of Resource Provisioning Cost in Cloud Computing


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The slide is about how we can optimally provision servers with combination of reservation and on-demand plans offered by multiple cloud providers. The slide content is from the journal paper published in IEEE Transactions on Service Computing

It was firstly presented in PDCC, School of Computer Engineering, Nanyang Technological University, Singapore.

Published in: Education, Technology, Business
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Optimization of Resource Provisioning Cost in Cloud Computing

  1. 1. Optimization of Resource ProvisioningCost in Cloud Computingpresented by Sivadon Chaisiri (Boom)1supervised by Assoc Prof. Bu Sung Lee1,2 and Asst Prof. Dusit Niyato1 Assoc. Prof Bu-Sung Asst. Prof1School of Computer Engineering, Nanyang Technological University, Singapore2HP Labs Singaporepresented in PDCC Seminar, Parallel & Distributed Computing Centre, Friday 21st, 2011
  2. 2. About This PresentationBased on th f ll i under-review paperB d the following d i S. Chaisiri, B. S. Lee, and D. Niyato, Optimization of Resource Provisioning Cost in Cloud Computing, submitted to IEEE Transactions on Services C T ti S i Computing (TSC) submitted i M h ti (TSC), b itt d in March 2010, received major revision in September 2010, received minor revision on Christmas 2010, [Still under review], Accepted in February 2011 to appear 2011,Other relevant published conference papers S. Chaisiri, B. S. Lee, and D. Niyato, Robust Cloud Resource Provisioning for Cloud Computing Environments, presented in IEEE Int. Conf. on Service Oriented Computing and Applications (SOCA), Perth, Australia, December 2010 S. Chaisiri, B. S. Lee, and D. Niyato, Optimal Virtual Machine Placement across Multiple Cloud Providers, presented in IEEE Asia-Pacific Services Computing Conference (APSCC), Singapore, December 2009 2
  3. 3. OutlineOverview of Cloud Computing Provisioning plansChallenge of Resource Provision in the Cloud UncertaintyOptimal Cloud Resource Provisioning (OCRP) OCRP Model OCRP Formulation Benders Decomposition Sample Average Approximation Numerical ResultsSummary 3
  4. 4. Overview of Cloud ComputingSoftware Hardware infrastructure • Large distributed system Pool of resources • Large pool of resources Physical compute resources • Multiple providerStorage Network • Multiple data-centers • Virtualization Cloud Cloud Cloud Cloud Cloud • Internet access provider provider provider provider provider • Pay-per-use basis Cloud Computing • Provisioning options/plans • On-demand • Reservation • Examples: E l Amazon, A GoGrid, G G id Cloud Cloud Cloud Cloud Cloud consumer consumer consumer consumer consumer Flexiscale, Rackspace, … 4
  5. 5. Overview of Cloud Computing: Provisioning plans• O d On-demand plan offered b A d l ff d by Amazon EC2• Reservation plan offered by Amazon EC2 Reservation can reduce the total provisioning cost On-demand (Small Instance): 0.085x365x24 = $744.60 for 1yr contract Reservation: 227.50+(0.03x365x24) = $490.30 for 1yr contract or 34% cheaper b 49% cheaper f 3 contract h but h for 3yrSource: http://aws.amazon.com/ec2 5
  6. 6. Challenge of Resource Provision in the Cloud• Resource provision = activity to provide / supply resource (to accommodate users/systems)• Goal: How many VMs (i.e., how much resource) do we need to provision in advance (i.e., provision with reservation plan) ?• Challenge Multiple l d M lti l cloud providers & Q S & SLA id QoS Multivariate uncertainty e.g., demand, price, availability Optimal solution under uncertainty Computational complexityNote VMs = virtual machine or (virtualized) servers with installed required software 6
  7. 7. Challenge of Resource Provision in the Cloud: Uncertainty• Uncertainty of price • On-demand price might be fluctuated p g• Uncertainty of availability • Free / cheap resources offered by a cloud provider might be provided based on weak SLAs • Internet bandwidth is not reliable until cloud resources might not be accessible 7
  8. 8. Challenge of Resource Provision in the Cloud: Uncertainty• U Uncertainty of demand t i t fd d Decision Realization time Reserve N VMs No on-demand Utilize N VMs provisioning Actual demand is N VMs (a) Best provisioning Decision Realization time Decision Realization timeReserve N VMs Provision 2N VMs Reserve N VMs No need Utilize N VMs Utilize N/2 VMs on-demand on-demand provisioning Actual demand is 3N VMs Actual demand is N/2 VMs (b) Underprovisioning problem (c) Overprovisioning problem Higher on-demand cost on demand Higher oversubscribed cost 8
  9. 9. Optimal Cloud Resource ProvisioningOCRP algorithm is proposedTo minimize the expected resource provisioning cost in multipleprovisioning stages e g 4 stages in quarter plan, 12 stages in e.g., plan1-Y plan, 36 stages in 3-Y plan, etc.To consider multivariate uncertaintyOptimal solution is obtained by formulating and solvingstochastic integer programming with multi-stage recourseTechniques to solve OCRP: deterministic equivalence equivalence,benders decomposition, sample-average approximationSeveral experiments show that OCRP can minimize thecost under uncertainty 9
  10. 10. OCRP Model System model of cloud computing Cloud providers Cloud provider’s infrastructure Virtual machines Cloud Cloud Cloud provider provider provider Physical compute resources Virtual machines Virtual Vi t l machines hi Cloud OCRP consumer Virtual machine Cloud broker repository 10
  11. 11. Provisioning Phases Provisioning phase: ti P i i i h time i t interval when resources l h need to be provisioned or utilized 1. Reservation phase: reserve resources 2. Expending phase: utilized the reserved resources 3. On-demand phase: provision more resources on-demand 11
  12. 12. Provisioning Stages Provisioning stage: ti P i i i t time epoch when cloud b k h h l d broker makes a decision Examples Two stages: current and future (e.g., now and next month) Twelve stages: Yearly plan = January to December 12
  13. 13. Reservation Contracts Reservation contract: signed contract stating the time duration of availability of reserved resource During the contract period, price is discounted Examples: 3-month (K1) and 6-month (K2) contracts 13
  14. 14. Uncertainty Stochastic programming requires uncertainty parameters, namely scenarios given by set Scenarios can be described by a probability distribution Set S has fi i support with probabilities h finite ih b bili i where 14
  15. 15. OCRP Formulation: stochastic programming model 15
  16. 16. OCRP Formulation: deterministic equivalence 16
  17. 17. Benders Decomposition• Benders decomposition breaks down an optimization problem i i i i bl into smaller ll problems which can be solved independently (p p y (parallelly) y)• Given the results obtained from master & subproblems, the lower & upper bounds of solution can b calculated b d f l ti be l l t d• Convergence bounds checked by where and 17
  18. 18. Benders Decomposition: master & subproblems• M Master problem bl• Subproblems 18
  19. 19. Sample-Average Approximation• If the number of scenarios ( ) is numerous, it may not be efficient to obtain the solution of OCRP• SAA addresses the problem by sampling N scenarios, then SAA b i h SAA-based OCRP f d formulation l i is solved given the N samples• W modeled OCRP b We d l d based on SAA approach t d h to choose N that yields the acceptable approximated solution• SAA can be parallelized as well 19
  20. 20. Sample-Average Approximation: SSA formulation 20
  21. 21. Numerical Results: provisioning cost 21
  22. 22. Numerical Results: comparisons 22
  23. 23. Numerical Results: samplingActual number scenarios is 244,140,625 but 750 samples pcan yield an acceptable solution 23
  24. 24. SummaryResource provisioning algorithms based on stochasticprogramming and robust optimization have been proposedThe l ithTh algorithms can be applied in real world t minimize b li d i l ld to i i iprovisioning costsResource management framework for cloud computing will g p gbe composedWork-in-progress: resource provisioning for hybrid cloud Scenario tree constr ction and red ction construction reduction Real data from HPC@NTU as a practical showcase Cost-benefit analysis between private cloud vs. hybrid cloudFuture work (just plan to do in this year) Framework for Map/Reduce on hybrid processors & hybrid cloud Cost-benefit-analysis of certain applications on the hybrid cloud 24
  25. 25. THANK YOUContact: siva0020@ntu.edu.sg ; sivadon@ieee.org http://about.me/javaboom 25
  26. 26. Major Contributions1. Optimal Virtual Machine Placement (OVMP) Two-stage recourse stochastic programming Optimal provisioning resources under uncertainty Optimal allocating VMs to cloud providers2. Optimal Cloud Resource Provisioning (OCRP) Multi-stage recourse stochastic programming Also modeling OCRP by Benders decomposition and sample-average approximation3. Robust Cloud Resource Provisioning (RCRP) Robust optimization modeling 26
  27. 27. Overview of Cloud Computing: Multiple data-centers• Multiple data-centers: Availability Zones organized by Amazon EC2 Picture courtesy of Amazon 27
  28. 28. Overview of Cloud Computing: Pay-per-use• C t of on-line storage offered by A Cost f li t ff d b Amazon S3 Source: http://aws.amazon.com/s3 28