Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud

• Published in: 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems pp. 85 - 95
• Main Authors: Chaisiri, S., Kaewpuang, R., Bu-Sung Lee, Niyato, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2011
• Subjects: Approximation algorithms; cloud computing; Computational modeling; Contracts; Optimization; Programming; robust optimization; sample-average approximation; Servers; stochastic programming; Uncertainty
• ISBN: 9781457704680; 1457704684
• ISSN: 1526-7539; 2375-0227
• DOI: 10.1109/MASCOTS.2011.30

Amazon Elastic Compute Cloud (EC2) provides a cloud computing service by renting out computational resources to customers (i.e., cloud users). The customers can dynamically provision virtual servers (i.e., computing instances) in EC2, and then the customers are charged by Amazon on a pay-per-use basis. EC2 offers three options to provision virtual servers, i.e., on-demand, reservation, and spot options. Each option has different price and yields different benefit to the customers. Spot price (i.e., price of spot option) could be the cheapest, however, the spot price is fluctuated and even more expensive than the prices of on-demand and reservation options due to supply-and-demand of available resources in EC2. Although the reservation and on-demand options have stable prices, their costs are mostly more expensive than that of spot option. The challenge is how the customers efficiently purchase the provisioning options under uncertainty of price and demand. To address this issue, two virtual server provisioning algorithms are proposed to minimize the provisioning cost for long- and short-term planning. Stochastic programming, robust optimization, and sample-average approximation are applied to obtain the optimal solutions of the algorithms. To evaluate the performance of the algorithms, numerical studies are extensively performed. The results show that the algorithms can significantly reduce the total provisioning cost incurred to customers.