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The influence of resource allocation on cloud computing performance

Published: 03 April 2017 Publication History

Abstract

This paper makes experimental evaluations that involve the allocation of virtual machines in a cloud environment. Virtual machine allocation is an open research field in cloud, which can lead to the best performance for clients. However, the allocations are made by estimating the number of resources that need to be allocated to the virtual machines in the host without taking account of the possible workload required for these virtual machines. In carrying out this, we set up a cloud prototype, together with virtual machines with the same configuration as that for Amazon and Microsoft providers, so that our prototype could be validated. After this, we allocated as many virtual machines as possible in a single host based on our own infrastructure and involving homogeneous workloads and heterogeneous workloads. The results showed that the benefits obtained from heterogeneous sets of virtual machines were better than the homogeneous sets.

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cover image ACM Conferences
SAC '17: Proceedings of the Symposium on Applied Computing
April 2017
2004 pages
ISBN:9781450344869
DOI:10.1145/3019612
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 03 April 2017

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Author Tags

  1. cloud computing
  2. resource allocation
  3. virtualization

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SAC 2017
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SAC 2017: Symposium on Applied Computing
April 3 - 7, 2017
Marrakech, Morocco

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
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