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Resource Management in Utility and Cloud ComputingOptimal Resource Rental Management

Resource Management in Utility and Cloud Computing: Optimal Resource Rental Management [Application services using cloud computing infrastructure are proliferating over the Internet. In this chapter, we study the problem of how to minimize resource rental cost associated with hosting such cloud-based application services, while meeting the projected service demand. This problem arises when applications incur significant storage and network transfer cost for data. Therefore, an Application Service Provider (ASP) needs to carefully evaluate various resource rental options before finalizing the application deployment. We choose Amazon®; EC2 marketplace as a case of study, and analyze the optimal strategy that exploits the tradeoff of data caching versus computing on demand for resource rental planning in cloud. Given fixed resource pricing, we first develop a deterministic model, using a mixed integer linear program, to facilitate resource rental decision making. Next, we investigate planning solutions to a resource market featuring time-varying pricing. We conduct time-series analysis over the spot price trace and examine its predictability using Auto-Regressive Integrated Moving-Average (ARIMA). We also develop a stochastic planning model based on multistage recourse. By comparing these two approaches, we discover that spot price forecasting does not provide our planning model with a crystal ball due to the weak correlation of past and future price, and the stochastic planning model better hedges against resource pricing uncertainty than resource rental planning using forecast prices.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Resource Management in Utility and Cloud ComputingOptimal Resource Rental Management

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Publisher
Springer New York
Copyright
© The Author(s) 2013
ISBN
978-1-4614-8969-6
Pages
15 –36
DOI
10.1007/978-1-4614-8970-2_2
Publisher site
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Abstract

[Application services using cloud computing infrastructure are proliferating over the Internet. In this chapter, we study the problem of how to minimize resource rental cost associated with hosting such cloud-based application services, while meeting the projected service demand. This problem arises when applications incur significant storage and network transfer cost for data. Therefore, an Application Service Provider (ASP) needs to carefully evaluate various resource rental options before finalizing the application deployment. We choose Amazon®; EC2 marketplace as a case of study, and analyze the optimal strategy that exploits the tradeoff of data caching versus computing on demand for resource rental planning in cloud. Given fixed resource pricing, we first develop a deterministic model, using a mixed integer linear program, to facilitate resource rental decision making. Next, we investigate planning solutions to a resource market featuring time-varying pricing. We conduct time-series analysis over the spot price trace and examine its predictability using Auto-Regressive Integrated Moving-Average (ARIMA). We also develop a stochastic planning model based on multistage recourse. By comparing these two approaches, we discover that spot price forecasting does not provide our planning model with a crystal ball due to the weak correlation of past and future price, and the stochastic planning model better hedges against resource pricing uncertainty than resource rental planning using forecast prices.]

Published: Sep 19, 2013

Keywords: Cloud Computing; Virtual Machine; Mixed Integer Linear Program; Spot Price; Spot Prex

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