Robust Cloud Resource Provisioning for Cloud Computing Environments


Published on

Published in: Education
1 Comment
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Robust Cloud Resource Provisioning for Cloud Computing Environments

  1. 1. Robust Cloud Resource Provisioningfor Cloud Computing Environmentspresented bySivadon Chaisiri1, Bu-Sung Lee1,2, and Dusit Niyato11 School of Computer Engineering, Nanyang Technological University, Singapore2 HP Labs Singaporepresented inIEEE International Conference on Service-Oriented Computing and Applications (SOCA’10) Perth, Australia, December 14, 2010
  2. 2. Outline● Overview of Cloud Computing ● Provisioning plans● Challenge of Resource Provisioning● Robust Cloud Resource Provisioning ● Modeling the RCRP ● Formulating the RCRP● Numerical Studies● Conclusion 2
  3. 3. Overview of Cloud Computing HardwareSoftware infrastructure Pool of resources Cloud Computing • Large distributed system Physical compute resources • Large pool of resourcesStorage Network • Multiple providers • Amazon EC2 Cloud Cloud Cloud Cloud Cloud • GoGrid provider provider provider provider provider • Rackspace Cloud Computing • Virtualization (e.g., IaaS) • Internet access • Pay-per-use basis • Provisioning plans Cloud consumer Cloud consumer Cloud consumer Cloud consumer Cloud consumer • On-demand • Reservation 3
  4. 4. Provisioning Plans● On-demand plan offered 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 34.15% cheaper but 49.04% cheaper for 3yr contract 4
  5. 5. Challenge of Resource Provision ● Goal: How many VMs do we need to provision in advance to minimize the total cost under uncertainty? ● Challenge: ● Multivariate uncertainty e.g., price, demand, availability, etc. ● Unavoidable under- and overprovisioning costs ● Multiple providers + service-level-agreements (SLAs) Decision Realization Reserve N VMs Utilize N VMs (no more cost) Actual demand is N VMs (a) Best provisioning Decision Realization Decision Realization No need Reserve N VMs Utilize N/2 VMsReserve N VMs Provision 2N VMs on-demand Utilize N VMs on-demand provisioning (on demand cost) (oversubscribed cost) Actual demand is 3N VMs Actual demand is N/2 VMs (b) Underprovisioning problem (c) Overprovisioning problem 5
  6. 6. Robust Cloud Resource Provisioning● RCRP algorithm is proposed ● Minimize the expected resource provisioning cost ● Reduce on-demand & oversubscribed costs ● Consider multivariate uncertainty ● Meet the decision maker’s risk preference: most decision makers are risk averse● Two types of robustness ● Solution robustness: solution is almost optimal ● Model robustness: penalty is almost avoided 6
  7. 7. Modeling the RCRP● System model of cloud computing 7
  8. 8. Modeling the RCRP (cont...)● Multiple IaaS-based cloud providers● Provisioning plans: reservation & on-demand● Each cloud provider offers different plans, prices, and service-level-agreement (SLA)● VM class = group of VMs executing the same job● Each VM class requires different resources● Demand = the number of VMs of specific VM class required to execute the cloud consumers job 8
  9. 9. Modeling the RCRP (cont...)● Provisioning phases: reservation, expending, on-demand● Two provisioning stages (namely first and second)● Uncertain parameter is described by probability distribution● Realization = observed uncertain parameter● Recourse action = the action corresponding to certain realization● (Optimal) Solution consists of ● The number of reserved VMs provisioned for each VM class ● A collection of recourse actions 9
  10. 10. Formulating the RCRP• Complete RCRP model 10
  11. 11. Formulating the RCRP (cont…)• Multi-criteria optimization• Total resource provisioning cost:• Solution robustness: cost of deviation with weight :• Model robustness: penalty function cost with weight : 11
  12. 12. Formulating the RCRP (cont...) Solution robustness Model robustness● Adjustment of weights to meet the risk preference• Weighting to adjust the solution robustness• Guideline for adjusting the model robustness ● Weighting and to adjust the model robustness: ● : overprovisioning weights ● : underprovisioning weights ● Simplifying over- and underprovisioning weights ● Let ● where 12
  13. 13. Numerical Studies: Parameter Setting• Two VM classes (I1 and I2) require difference resources• Max resource capacity offered by cloud providers (J1 to J4): ● J1 (private cloud) offers limited resources but zero on-demand cost ● J2 to J4 (public clouds) offer abundant resources• Pricing defined by each cloud provider:• Three types of uncertain parameters are considered – Types: users demand, resource price, resource availability – Each type is described by different probability distribution• RCRP and other models are implemented and solved by GAMS/CPLEX 13
  14. 14. Numerical Studies: Results 14
  15. 15. Numerical Studies: Results (cont...)● Comparison between RCRP and others● Summary of the comparison: ● NoRes yields the highest total cost ● MaxRes has zero on-demand but highest oversubscribed ● EVU gains the lowest oversubscribed but high on-demand ● OVMP achieves the minimum total cost ● RCRP is more flexibly controlled and it can achieve the total cost close to OVMP 15
  16. 16. Conclusion● Due to uncertainty, inefficiency of resource provisioning can lead to very expensive costs● RCRP is proposed to minimize the total provisioning cost, while uncertainty is considered● RCRP can achieve both solution- and model- robustness● RCRP can meet decision makers risk preferences● RCRP can be applied in the real practice● Future work: sampling techniques and real practice will be performed 16
  17. 17. THANK YOU 17
  18. 18. Formulating the RCRP (cont…)• Stochastic programming (SP) model• This SP could only satisfy low-risk decisions• SP cannot be adjusted to meet the risk preference 18
  19. 19. Numerical Studies: Results (cont...) How to choose the appropriate solution? 1) Apply goal programming based on a predefined goal such as ● Expected reservation cost <= $1,200 ● Expected on-demand cost <= $1,000 ● Stand deviation of RO must be less than SP 2) Vary the weights and solve the RCRP until the goal is met Selected solution: = 1 and =1 19