Robust Cloud Resource Provisioning for Cloud Computing Environments
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This document presents the Robust Cloud Resource Provisioning (RCRP) algorithm. RCRP aims to minimize cloud computing resource provisioning costs under uncertainty. It considers multiple cloud providers, provisioning plans, resource requirements, and uncertain demand and prices. RCRP formulates the problem as a multi-criteria optimization to balance total cost, solution robustness, and model robustness. Numerical studies show RCRP achieves costs close to optimal methods while allowing flexibility to meet risk preferences, unlike stochastic programming approaches. The RCRP algorithm provides a robust approach for resource provisioning under uncertainty in cloud computing environments.
Robust Cloud Resource Provisioning for Cloud Computing Environments
- 1. Robust Cloud Resource Provisioning for Cloud Computing Environments presented by Sivadon Chaisiri1, Bu-Sung Lee1,2, and Dusit Niyato1 1 School of Computer Engineering, Nanyang Technological University, Singapore 2 HP Labs Singapore presented in IEEE International Conference on Service-Oriented Computing and Applications (SOCA’10) Perth, Australia, December 14, 2010
- 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. Overview of Cloud Computing Hardware Software infrastructure Pool of resources Cloud Computing • Large distributed system Physical compute resources • Large pool of resources Storage 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. 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. 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 VMs Reserve 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. 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. Modeling the RCRP ● System model of cloud computing 7
- 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 consumer's job 8
- 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. Formulating the RCRP • Complete RCRP model 10
- 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. 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. 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: user's 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. Numerical Studies: Results 14
- 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. 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. THANK YOU 17
- 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. 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