Title :
A Multi-resource Sharing-Aware Approximation Algorithm for Virtual Machine Maximization
Author :
Rampersaud, Safraz ; Grosu, Daniel
Author_Institution :
Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
Abstract :
Cloud providers face the challenge of efficiently managing their infrastructure through minimizing resource consumption while allocating requests such that their profit is maximized. We address this challenge by designing a greedy approximation algorithm for solving the multi-resource sharing-aware virtual machine maximization (MSAVMM) problem. The MSAVMM problem requires determining the set of VMs that can be instantiated on a given server such that the profit derived from hosting the VMs is maximized. The solution to this problem has to consider the sharing of memory pages among VMs and the restricted capacities of each type of resource requested by the VMs. We analyze the performance of the proposed algorithm by determining its approximation ratio and by performing extensive experiments against other sharing-aware VM allocation algorithms.
Keywords :
approximation theory; cloud computing; greedy algorithms; resource allocation; virtual machines; MSAVMM problem; approximation ratio; cloud providers; greedy approximation algorithm; infrastructure management; memory page sharing; multiresource sharing-aware approximation algorithm; multiresource sharing-aware virtual machine maximization problem; request allocation; resource consumption minimization; sharing-aware VM allocation algorithm; Approximation algorithms; Approximation methods; Greedy algorithms; Measurement; Resource management; Servers; Virtual machine monitors; approximation ratio; greedy algorithms; multi-resource; resource allocation; virtual machine;
Conference_Titel :
Cloud Engineering (IC2E), 2015 IEEE International Conference on
Conference_Location :
Tempe, AZ
DOI :
10.1109/IC2E.2015.20