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A Cost-Optimized Data Parallel Task Scheduling in Multi-Core Resources Under Deadline and Budget Constraints

Author

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  • Saravanan Krishnan

    (Anna University, Tirunelveli, India)

  • Rajalakshmi N. R.

    (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India)

Abstract

Large-scale distributed systems have advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data through distributed computing system becomes obscure because the major concern in large-scale distributed systems is to guarantee the completion of data processing task to be done within a budget and time constraints. This paper proposes a cost-optimized data parallel task scheduling in multi-core resources to address the above issue. By running concurrent executions on a multi-core resource, the number of parallel executions could be increased correspondingly, thereby it is able to finish the task within the deadline. A model is developed here to optimize the operational cost of data parallel task by feasibly assigning load fractions to each multi-core resource. This work experimented with data parallel task. The outcome of the work gives better solutions in terms of processing task by deadline at optimised computational cost.

Suggested Citation

  • Saravanan Krishnan & Rajalakshmi N. R., 2022. "A Cost-Optimized Data Parallel Task Scheduling in Multi-Core Resources Under Deadline and Budget Constraints," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(2), pages 1-16, April.
    • Handle: RePEc:igg:jcac00:v:12:y:2022:i:2:p:1-16
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    Cited by:

    1. Zhou, Yufei & Wang, Sihan & Zhang, Nuo, 2023. "Dynamic decision-making analysis of Netflix's decision to not provide ad-supported subscriptions," Technological Forecasting and Social Change, Elsevier, vol. 187(C).

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