Gamal F. Elhady

Work place: Faculty of Computers and Information's, Menofia University / Computer Science Department, Menofia, Egypt



Research Interests: Artificial Intelligence, Image Compression, Image Manipulation, Distributed Computing, Image Processing, Data Mining, Database Management System


Gamal. F. Elhady: received the B.S, M.S and Ph.D degree in Computer Science at Faculty of Science, in 1998 and 2006, Mansoura University, Egypt. During 1998 and 2006, he works as the researcher student and Lecturer Assistance in Faculty of science computer science Dept. He is member of IAENG in USA (# 108463). His research interest includes software programing, software testing, distributed system, data mining, database, Artificial intelligent, image processing and bioinformatics.

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

By Medhat A. Tawfeeq Gamal F. Elhady

DOI:, Pub. Date: 8 Nov. 2016

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.

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