Robust cloud resource provisioning for cloud computing environments

• Published in: 2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA) pp. 1 - 8
• Main Authors: Chaisiri, S, Bu-Sung Lee, Niyato, D
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2010
• Subjects: Cloud computing; Computational modeling; Numerical models; Optimization; Programming; resource provisioning; robust optimization; Robustness; stochastic programming; Uncertainty
• ISBN: 9781424498024; 1424498023
• ISSN: 2163-2871; 2689-7121
• DOI: 10.1109/SOCA.2010.5707147

Cloud providers can offer cloud consumers two plans to provision resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, this resource provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' resource prices can be fluctuated. Moreover, inefficiency of resource provisioning leads to either overprovisioning or underprovisioning problem. In this paper, we propose a robust cloud resource provisioning (RCRP) algorithm to minimize the total resource provisioning cost (i.e., overprovisioning and underprovisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total resource provisioning cost is close to the optimality (i.e., solution-robustness), and the overprovisioning and underprovisioning costs are significantly reduced (i.e., model-robustness).