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Frequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
Home Archives Volume 6 Issue 2 ijcatr06021003

IJCATR Volume 6 Issue 2

Resource Allocation in Cloud Environment Using Approaches Based Particle Swarm Optimization

Vahid Asadzadeh Chalack Seyed Naser Razavi Sajjad Jahanbakhsk Gudakahriz
10.7753/IJCATR0602.1003
keywords : Resources Allocation, Cloud computing, Particle swarm optimization, Makespan, Flowtime.

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It is obvious that in emerging computing paradigms such as cloud computing systems, scheduling is one of the main phases to take advantages of capabilities. The cloud computing environment is a dynamic environment which allows services to be shared among many users. Scheduling methods of traditional systems are ill-suited for the cloud computing systems, and this new environment requires new methods tailored to its specifications. In this paper, we developed multiple algorithms for task scheduling in cloud computing systems. These algorithms are based on the particle swarm optimization (PSO) algorithm, which is a technique inspired by collective and social behavior of animal swarms in nature, and wherein particles search the problem space to find an optimal or near-optimal solution. The algorithms were developed with the aim of minimizing Makespan, Flowtime and the task execution cost simultaneously. Simulation and test results show the better efficiency of the proposed methods than other similar algorithms.
@artical{v622017ijcatr06021003,
Title = "Resource Allocation in Cloud Environment Using Approaches Based Particle Swarm Optimization",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "6",
Issue ="2",
Pages ="87 - 90",
Year = "2017",
Authors ="Vahid Asadzadeh Chalack Seyed Naser Razavi Sajjad Jahanbakhsk Gudakahriz"}
  • we developed multiple algorithms for task scheduling in cloud computing systems.
  • The algorithms were developed with the aim of minimizing Makespan, Flowtime and the task execution cost simultaneously.
  • The task scheduling problem consists of N tasks and M machines.
  • The proposed algorithm utilized two high speed PSO-based methods which improved resource efficiency.
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