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Mathematical Problems in Engineering
Volume 2014, Article ID 215016, 11 pages
http://dx.doi.org/10.1155/2014/215016
Research Article

Coarse-Grain QoS-Aware Dynamic Instance Provisioning for Interactive Workload in the Cloud

School of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China

Received 22 November 2013; Revised 22 January 2014; Accepted 23 January 2014; Published 25 March 2014

Academic Editor: Huiping Li

Copyright © 2014 Jianxiong Wan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cloud computing paradigm renders the Internet service providers (ISPs) with a new approach to deliver their service with less cost. ISPs can rent virtual machines from the Infrastructure-as-a-Service (IaaS) provided by the cloud rather than purchasing them. In addition, commercial cloud providers (CPs) offer diverse VM instance rental services in various time granularities, which provide another opportunity for ISPs to reduce cost. We investigate a Coarse-grain QoS-aware Dynamic Instance Provisioning (CDIP) problem for interactive workload in the cloud from the perspective of ISPs. We formulate the CDIP problem as an optimization problem where the objective is to minimize the VM instance rental cost and the constraint is the percentile delay bound. Since the Internet traffic shows a strong self-similar property, it is hard to get an analytical form of the percentile delay constraint. To address this issue, we purpose a lookup table structure together with a learning algorithm to estimate the performance of the instance provisioning policy. This approach is further extended with two function approximations to enhance the scalability of the learning algorithm. We also present an efficient dynamic instance provisioning algorithm, which takes full advantage of the rental service diversity, to determine the instance rental policy. Extensive simulations are conducted to validate the effectiveness of the proposed algorithms.