Issue No. 02 - Second (2012 vol. 5)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TSC.2011.7
Sivadon Chaisiri , Nanyang Technological University, Singapore
Bu-Sung Lee , Nanyang Technological University, Singapore
Dusit Niyato , Nanyang Technological University, Singapore
In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.
Cloud computing, resource provisioning, virtualization, virtual machine placement, stochastic programming.
S. Chaisiri, B. Lee and D. Niyato, "Optimization of Resource Provisioning Cost in Cloud Computing," in IEEE Transactions on Services Computing, vol. 5, no. , pp. 164-177, 2011.