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首页> 外文期刊>International journal of applied evolutionary computation >PSO:A Novel Meta-Heuristics for Load Balancing in Cloud Computing
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PSO:A Novel Meta-Heuristics for Load Balancing in Cloud Computing

机译:PSO:云计算中负载均衡的新型元启发式方法

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摘要

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better Quality of Service (QOS). It is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a load balancing algorithm using Multi Particle Swarm Optimization (MPSO) has been developed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization in a homogenous cloud environment. Performance comparisons are made with Genetic Algorithm (GA), Multi GA, PSO and other popular algorithms on different measures like makespan calculation and resource utilization.
机译:云计算由于其比常规计算的优势而越来越受欢迎。它按需向订户提供基于公用事业的服务。云托管各种Web应用程序,并提供按使用付费的服务。随着用户在云系统中的增长,负载平衡已成为一个关键问题。在各个节点之间的云环境中调度工作负载对于实现更好的服务质量(QOS)至关重要。分配具有可变容量和功能的资源是一个突出的研究领域,也是一个挑战。本文利用粒子群优化(PSO)算法的优势,开发了一种使用多粒子群优化(MPSO)的负载均衡算法。所提出的方法旨在最小化任务开销并在同构云环境中最大化资源利用率。使用遗传算法(GA),Multi GA,PSO和其他流行算法对不同的度量进行了性能比较,例如工期计算和资源利用。

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