Hybrid Algorithm Based on Swarm Intelligence Techniques for Dynamic Tasks Scheduling in Cloud Computing

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Medhat A. Tawfeeq 1,* Gamal F. Elhady 1

1. Faculty of Computers and Information's, Menofia University / Computer Science Department, Menofia, Egypt

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2016.11.07

Received: 10 Jan. 2016 / Revised: 11 May 2016 / Accepted: 20 Jul. 2016 / Published: 8 Nov. 2016

Index Terms

Cloud Computing, Task Scheduling, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Makespan, CloudSim


Cloud computing has its characteristics along with some important issues that should be handled to improve the performance and increase the efficiency of the cloud platform. These issues are related to resources management, fault tolerance, and security. The purpose of this research is to handle the resource management problem, which is to allocate and schedule virtual machines of cloud computing in a way that help providers to reduce makespan time of tasks. In this paper, a hybrid algorithm for dynamic tasks scheduling over cloud's virtual machines is introduced. This hybrid algorithm merges the behaviors of three effective techniques from the swarm intelligence techniques that are used to find a near optimal solution to difficult combinatorial problems. It exploits the advantages of ant colony behavior, the behavior of particle swarm and honeybee foraging behavior. Experimental results reinforce the strength of the proposed hybrid algorithm. They also prove that the proposed hybrid algorithm is the best and outperformed ant colony optimization, particle swarm optimization, artificial bee colony and other known algorithms.

Cite This Paper

Medhat A. Tawfeek, Gamal F. Elhady, "Hybrid Algorithm Based on Swarm Intelligence Techniques for Dynamic Tasks Scheduling in Cloud Computing", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.11, pp.61-69, 2016. DOI:10.5815/ijisa.2016.11.07


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