Affiliations: [a]
School of Economics and Management, Yanshan University, Qinhuangdao, China
| [b] State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
| [c]
School of Economics and Management, Communication University of China, Beijing, China
Correspondence:
[*]
Corresponding author: Jia Liu, State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China. E-mail: jialiu@cuc.edu.cn.
Abstract: The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme.
