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Energy-Efficient Task Scheduling in Fog Computing Based on Particle Swarm Optimization

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Abstract

Recently, continuous growth in use of Internet of Things (IoT) produces huge amount of data during processing, which increases load on Cloud Computing network. Cloud computing does not support low latency for real-time applications. To overcome such drawbacks like low latency, lower response time, IoT-based application moves towards Fog Computing. Fog Computing along with Cloud Computing provides best solution for such IoT applications. Fog Computing enables user to process data near to end user. It supports low latency and low response time than Cloud environment. Fog Computing allows to schedule application tasks on various Fog nodes which are available in the network. Fog nodes have limited resources than Cloud Computing, and hence, scheduling tasks among Fog nodes becomes essential. In this paper, a scheduling algorithm, namely, Energy efficient task scheduling in Fog Computing based on Particle Swarm Optimization (EETSPSO), has been proposed. The proposed EETSPSO algorithm simulated in MATLAB. Proposed EETSPSO evaluated using evaluation parameters makespan, energy, and execution time. The simulated result of EETSPSO shows better result by minimizing the makespan time, execution time, and reducing the energy consumption as compared to Bee Life Algorithm (BLA) and Modified Particle Swarm Optimization (MPSO). The simulated experiment shows better performance of EETSPSO for makespan 6.39% and 4.71% compared to MPSO and BLA respectively. Simulation result of EETSPSO also shows 9.12% and 11.47% energy reduction when compared with MPSO and BLA. EETSPSO performed better than MPSO and BLA by 9.83% and 6.32%, respectively, for execution time comparison.

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Data Availability

As we have used a simulator, so data was generated based on the model we have proposed. We can provide that data and code if required. No pre-existing data was used for the analysis of this model.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, North Cap University, Gurugram, Haryana, India

    Shilpa Dinesh Vispute

  2. Department of Computer Science and Engineering, Amity University, Gurugram, Haryana, India

    Priyanka Vashisht

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  1. Shilpa Dinesh Vispute
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  2. Priyanka Vashisht
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Correspondence to Priyanka Vashisht.

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This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited by Omer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano and Nishtha Kesswani.

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Vispute, S.D., Vashisht, P. Energy-Efficient Task Scheduling in Fog Computing Based on Particle Swarm Optimization. SN COMPUT. SCI. 4, 391 (2023). https://doi.org/10.1007/s42979-022-01639-3

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