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Performance Evaluation of Heuristics for Cloud Workload Balancing

Published: 04 June 2018 Publication History

Abstract

Cloud computing introduces a new level of flexibility and scalability for providers and clients, because it addresses challenges such as rapid change in Information Technology (IT) scenarios and the need to reduce costs and time in infrastructure management. However, to be able to offer quality of service (QoS) guarantees without limiting the number of requests accepted, providers must be able to dynamically and efficiently scale service requests to run on the computational resources available in the data centers. Load balancing is not a trivial task, involving challenges related to service demand, which can shift instantly, to performance modeling, deployment and monitoring of applications in virtualized IT resources. In this way, the aim of this paper is to develop and evaluate the performance of different load balancing heuristics for a cloud environment in order to establish a more efficient mapping between the service requests and the virtual machines that will execute them, and to ensure the quality of service as defined in the service level agreement. By means of experiments, it was verified that the proposed heuristics presented better results when compared with traditional and artificial intelligence heuristics.

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cover image ACM Other conferences
SBSI '18: Proceedings of the XIV Brazilian Symposium on Information Systems
June 2018
578 pages
ISBN:9781450365598
DOI:10.1145/3229345
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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Publication History

Published: 04 June 2018

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Author Tags

  1. Cloud Computing
  2. Quality of Service
  3. Workload Balancing

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  • Research-article
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  • Refereed limited

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SBSI'18
SBSI'18: XIV Brazilian Symposium on Information Systems
June 4 - 8, 2018
Caxias do Sul, Brazil

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Overall Acceptance Rate 181 of 557 submissions, 32%

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