Genetic Algorithms for Job Scheduling in Cloud Computing - Université de Lorraine Accéder directement au contenu
Article Dans Une Revue Studies in Informatics and Control Année : 2015

Genetic Algorithms for Job Scheduling in Cloud Computing

Résumé

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.

Dates et versions

hal-01783624 , version 1 (02-05-2018)

Identifiants

Citer

Mohammed-Albarra Hassan, Imed Kacem, Sébastien Martin, Izzeldin Osman. Genetic Algorithms for Job Scheduling in Cloud Computing. Studies in Informatics and Control, 2015, 24 (4), ⟨10.24846/v24i4y201503⟩. ⟨hal-01783624⟩
65 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More