A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Document Type : Original Article

Authors

Dept. of Computer Science and Eng., Faculty of Elect., Eng., Menoufia University.

Abstract

This paper presents a new hybrid approach, called ACOSA, for
cloudlets scheduling to enhance the scheduler behavior in Cloud
computing (CC) environment and to overcome the results
oscillation problem of the existing meta-heuristic scheduling
algorithms. The proposed approach combines both the Ant
Colony Optimization (ACO) and Simulated Annealing (SA)
algorithm to improve both quality of solutions and time complexity
of the scheduling algorithm. The proposed approach is evaluated
by using the well-known CloudSim, and the results are compared
with the ant colony and simulated annealing separately in terms of
schedule length, load balancing, and time complexity. It
decreases the schedule length by 29.75% with SA and 12.25%
with ACO. The ACOSA provides higher load balancing degree. It
improves the balancing degree ratio by 36.36% than SA and
12.13% than ACO algorithms.

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