An economic agent maximizing cloud provider revenues under a pay-as-you-book pricing model
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
Scheduling jobs in the cloud using on-demand and reserved instances
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Application-Centric resource provisioning for amazon EC2 spot instances
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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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.