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Optimal Virtual Machine Placement across Multiple Cloud Providers Sivadon Chaisiri  , Bu-Sung Lee, and Dusit Niyato School...
Outline <ul><li>Introduction </li></ul><ul><li>Optimal Virtual Machine Placement </li></ul><ul><li>System Model </li></ul>...
Introduction: This Paper <ul><li>Virtual machine placement  </li></ul><ul><ul><li>Allocate a virtual machine (VM) to a phy...
Introduction: Cloud Computing Software Storage Hardware  infrastructure Network <ul><ul><li>Cloud Computing </li></ul></ul...
Optimal Virtual Machine Placement  (OVMP) <ul><li>Based on Infrastructure-as-a-Service (IaaS) </li></ul><ul><li>Two paymen...
OVMP: System Model Diagram
OVMP: Assumption <ul><li>Provided resources: computing power, storage, network bandwidth, and electric power </li></ul><ul...
OVMP: Three Provisioning Phases <ul><li>First stage </li></ul><ul><ul><li>Reservation phase  = reserve cheaper resources i...
OVMP: 3 Possible Cases On-demand cost > 0 Oversubscribed cost > 0
Formulation :  Stochastic Integer Programming Cost in first stage Cost in second stage Constraints Utilization constraint ...
Formulation:  Deterministic Equivalence cost in the first stage cost in the second stage objective function constraints
<ul><li>Evaluations </li></ul><ul><ul><li>Numerical studies – solving in NEOS server </li></ul></ul><ul><ul><li>Simulation...
Parameter Setting <ul><li>Resources required by 3 VM classes (VM1, VM2, VM3) </li></ul><ul><li>Resources offered by 4 clou...
Evaluation: Numerical Studies <ul><li>Optimal solution in a simple environment </li></ul>Optimal number of reserved VMs = 31
Evaluation: Numerical Studies <ul><li>Virtual machine placement in different phases </li></ul>Optimal number of reserved V...
Evaluation: Numerical Studies <ul><li>Total cost under different variances and prices </li></ul>
Evaluation: Numerical Studies <ul><li>Comparison between OVMP (based on SIP) and EVF </li></ul>SIP = Our stochastic intege...
Conclusion <ul><li>We propose OVMP to tackle complexity and uncertainty to provision resources in the cloud  </li></ul><ul...
Thank you Contact us –  [email_address]
Deterministic Integer Programming
Different Variances Fixed mean = 25.50
Evaluation: Simulation <ul><li>Simulation result </li></ul><ul><li>When on-demand prices are doubled </li></ul>
Parameter Setting  <ul><li>Prices defined by each cloud provider </li></ul>
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Optimal Virtual Machine Placement across Multiple Cloud Providers

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Optimal Virtual Machine Placement across Multiple Cloud Providers

  1. 1. Optimal Virtual Machine Placement across Multiple Cloud Providers Sivadon Chaisiri , Bu-Sung Lee, and Dusit Niyato School of Computer Engineering Nanyang Technological University, Singapore Tuesday, December 8, 2009 Presented in IEEE Asia-Pacific Services Computing Conference (APSCC), Singapore
  2. 2. Outline <ul><li>Introduction </li></ul><ul><li>Optimal Virtual Machine Placement </li></ul><ul><li>System Model </li></ul><ul><li>Problem Formulation </li></ul><ul><li>Performance Evaluation </li></ul><ul><li>Conclusion </li></ul>
  3. 3. Introduction: This Paper <ul><li>Virtual machine placement </li></ul><ul><ul><li>Allocate a virtual machine (VM) to a physical machine </li></ul></ul><ul><li>Cloud computing – (public) utility service </li></ul><ul><ul><li>Cloud providers e.g., Amazon, Google, Microsoft, Salesforce etc. </li></ul></ul><ul><ul><li>Leveraging virtualization e.g., Infrastructure-as-a-Service (IaaS) </li></ul></ul><ul><ul><li>IaaS Providers e.g., Amazon, GoGrid, FlexiScale, Redplaid, … </li></ul></ul><ul><li>Our Work – Optimal Virtual Machine Placement (OVMP) </li></ul><ul><ul><li>Optimally allocate VMs to cloud providers </li></ul></ul><ul><ul><li>Optimally advance reserve resources </li></ul></ul><ul><ul><li>Also consider uncertainty of demands and prices </li></ul></ul>
  4. 4. Introduction: Cloud Computing Software Storage Hardware infrastructure Network <ul><ul><li>Cloud Computing </li></ul></ul><ul><ul><li>Large distributed system </li></ul></ul><ul><ul><li>Large pool of resources </li></ul></ul><ul><ul><li>Multiple providers </li></ul></ul><ul><ul><li>Virtualization </li></ul></ul><ul><ul><li>Internet access </li></ul></ul><ul><ul><li>Pay-per-use basis </li></ul></ul><ul><ul><li>On-demand provisioning </li></ul></ul>
  5. 5. Optimal Virtual Machine Placement (OVMP) <ul><li>Based on Infrastructure-as-a-Service (IaaS) </li></ul><ul><li>Two payment plans to provision resources </li></ul><ul><ul><li>Reservation plan: cheaper but may not meet actual demand </li></ul></ul><ul><ul><li>On-demand plan: dynamically provisioning resources </li></ul></ul><ul><ul><li>e.g., Amazon EC2 and GoGrid </li></ul></ul><ul><li>Under price and demand uncertainty, OVMP algorithm can minimize the cost spending in each plan </li></ul><ul><ul><li>Optimally reserve resources </li></ul></ul><ul><ul><li>Optimally allocate VMs to cloud providers </li></ul></ul><ul><li>OVMP is achieved by stochastic integer programming with two stage recourse </li></ul>
  6. 6. OVMP: System Model Diagram
  7. 7. OVMP: Assumption <ul><li>Provided resources: computing power, storage, network bandwidth, and electric power </li></ul><ul><li>Price in the reservation plan is cheaper than that in the on-demand plan </li></ul><ul><li>VM class represents a distinct type of applications </li></ul><ul><li>Each VM class has different resource requirement </li></ul><ul><li>The number of VMs in each VM class depends on the demand from the user </li></ul><ul><li>Reserved VMs = reserved resources to execute a certain number of VMs </li></ul>
  8. 8. OVMP: Three Provisioning Phases <ul><li>First stage </li></ul><ul><ul><li>Reservation phase = reserve cheaper resources in advance </li></ul></ul><ul><li>Second stage </li></ul><ul><ul><li>Utilization phase = utilize the reserved resources </li></ul></ul><ul><ul><li>On-demand phase = pay for additional resources </li></ul></ul>[optimal reservation] [optimal allocation]
  9. 9. OVMP: 3 Possible Cases On-demand cost > 0 Oversubscribed cost > 0
  10. 10. Formulation : Stochastic Integer Programming Cost in first stage Cost in second stage Constraints Utilization constraint Demand constraint Capacity constraint Boundary constraint Objective Function Number of VMs of class allocated to provider under realization in utilization phase Number of VMs of class allocated to provider under realization in on-demand phase Number of VMs of class , reserved from provider Cost for reserving VMs of class from provider
  11. 11. Formulation: Deterministic Equivalence cost in the first stage cost in the second stage objective function constraints
  12. 12. <ul><li>Evaluations </li></ul><ul><ul><li>Numerical studies – solving in NEOS server </li></ul></ul><ul><ul><li>Simulation – simulating by MATLAB </li></ul></ul><ul><li>Assumption </li></ul><ul><ul><li>4 cloud providers and 3 VM classes </li></ul></ul><ul><ul><li>Required number of VMs is the same for all VM classes </li></ul></ul><ul><ul><li>Required number of VMs = {1,2, …, 50} </li></ul></ul><ul><ul><li>Probability distributions = normal dist., uniform dist., and dist. from test data * </li></ul></ul>Performance Evaluation * Test data was obtained from Institute of High Performance Computing (IHPC)
  13. 13. Parameter Setting <ul><li>Resources required by 3 VM classes (VM1, VM2, VM3) </li></ul><ul><li>Resources offered by 4 cloud providers (P1, P2, P3, P4) </li></ul><ul><li>Prices defined by each provider (not shown here) </li></ul>VM1 VM2 VM3 CPU-hours 12 18 24 Storage (GBs/day) 20 5 10 Network bandwidth (GBs/day) 33.33 66.67 266.67 P1 P2 P3 P4 CPU-hours 480 480 1,200 1,200 Storage (GBs/day) 1,000 1,000 1,000 1,000 Network bandwidth (GBs/day) 6.67 6.67 6.67 6.67
  14. 14. Evaluation: Numerical Studies <ul><li>Optimal solution in a simple environment </li></ul>Optimal number of reserved VMs = 31
  15. 15. Evaluation: Numerical Studies <ul><li>Virtual machine placement in different phases </li></ul>Optimal number of reserved VMs = 29 * Total cost = Reservation cost + Utilization cost + On-demand cost
  16. 16. Evaluation: Numerical Studies <ul><li>Total cost under different variances and prices </li></ul>
  17. 17. Evaluation: Numerical Studies <ul><li>Comparison between OVMP (based on SIP) and EVF </li></ul>SIP = Our stochastic integer programming formulation EVF = Expected-value formulation
  18. 18. Conclusion <ul><li>We propose OVMP to tackle complexity and uncertainty to provision resources in the cloud </li></ul><ul><li>With OVMP, the tradeoff between the advance reservation and the allocation of on-demand resources is adjusted to be optimal </li></ul><ul><li>Future work: OVMP with multiple decision stages </li></ul>
  19. 19. Thank you Contact us – [email_address]
  20. 20. Deterministic Integer Programming
  21. 21. Different Variances Fixed mean = 25.50
  22. 22. Evaluation: Simulation <ul><li>Simulation result </li></ul><ul><li>When on-demand prices are doubled </li></ul>
  23. 23. Parameter Setting <ul><li>Prices defined by each cloud provider </li></ul>

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