Efficient job scheduling algorithms needed to improve the resource utilization in cloud computing, the role of a good scheduling algorithm on cloud computing is to minimize the total completion time for last job on the system. In this paper, we present a genetic-based task scheduling algorithms in order to minimize Maximum Completion Time Makespan. These algorithms combines different techniques such as list scheduling and earliest completion time (ECT) with genetic algorithm. We reviewed, evaluated and compared the proposed algorithms against one of the well-known Genetic Algorithms available in the literature, which has been proposed for task scheduling problem on heterogeneous computing systems. After an exhaustive computational analysis we identify that the proposed Genetic algorithms show a good performance overcoming the evaluated method in different problem sizes and complexity for a large benchmark set of instances.
* This paper recalls the Task Scheduling Genetic Algorithm (GATS), published in [20]. In the current paper, we improve GATS and we propose an advanced version GATS+. Moreover, we propose two new algorithms: Genetic Algorithm Based on Cut-point (GACP) and Genetic Algorithm Based on The List of Available Jobs (GAAV), as well as its improved version (GAAV+). We also tested and compared different versions of these genetic algorithms (GAAV -> GATS, GAAV+ -> GATS, GATS -> GAAV, GATS -> GAAV+).
Task scheduling, Genetic Algorithm, Cloud Computing, Unrelated Parallel machines with precedence Constraints.
Mohammed-Albarra HASSAN, Imed KACEM, Sébastien MARTIN, Izzeldin M. OSMAN, "Genetic Algorithms for Job Scheduling in Cloud Computing*", Studies in Informatics and Control, ISSN 1220-1766, vol. 24(4), pp. 387-399, 2015. https://doi.org/10.24846/v24i4y201503
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