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Computers 2017, 6(2), 15; doi:10.3390/computers6020015

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Computer Science Department, University of Bahrain, Sakhir, Bahrain
Computer Engineering Department, University of Bahrain, Sakhir, Bahrain
Author to whom correspondence should be addressed.
Academic Editor: Paolo Bellavista
Received: 14 February 2017 / Revised: 19 March 2017 / Accepted: 29 March 2017 / Published: 5 April 2017
View Full-Text   |   Download PDF [4153 KB, uploaded 5 April 2017]   |  


In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality. View Full-Text
Keywords: cloud computing; real-time systems; task scheduling; genetic algorithms cloud computing; real-time systems; task scheduling; genetic algorithms

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mahmood, A.; Khan, S.A. Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm. Computers 2017, 6, 15.

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