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Fisher linear discriminant and discrete global swarm based task scheduling in cloud environment

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Abstract

Cloud computing (CC) environment delivers the services requested by the computing devices over the internet. In recent years with the internet epoch, the cloud computing environment is progressed as a significant distributed platform. However, the prime concern associated with the CC environment as task scheduling. Several algorithms were proposed with the objective of optimizing the scheduling process in the CC environment. To fill this gap in this work an integrated Fisher linear discriminant and discrete global swarm-based task scheduling (FLD-DGSTS) method is proposed. Fisher linear discriminant independent task prioritizer algorithm is employed to accurately enhance the quality of solution with minimum makespan and memory. Next, the discrete glowworm swarm optimization process is applied for scheduling cloud user tasks. The fitness function (i.e., CPU cycles, bandwidth, memory, and energy) is measured for addressing optimization and avoiding local convergence. The experimental evaluation of the proposed FLD-DGSTS method is compared to existing task scheduling algorithms with the CloudSim toolkit. The results demonstrate that the FLD-DGSTS method gives better performance with reduces the makespan and memory and ensures high scheduling efficiency than the state-of-the-art task scheduling methods.

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Authors and Affiliations

  1. Department of Computer Science, South Travancore Hindu College, Nagercoil, Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India

    K. M. Ajitha

  2. Department of Computer Science and Application, South Travancore Hindu College, Nagercoil, Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, 627012, Tamil Nadu, India

    N. Chenthalir Indra

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  1. K. M. Ajitha
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Correspondence to K. M. Ajitha.

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Ajitha, K.M., Indra, N.C. Fisher linear discriminant and discrete global swarm based task scheduling in cloud environment. Cluster Comput 25, 3145–3160 (2022). https://doi.org/10.1007/s10586-021-03509-8

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  • DOI: https://doi.org/10.1007/s10586-021-03509-8

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