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HOGWO: a fog inspired optimized load balancing approach using hybridized grey wolf algorithm

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Abstract

A distributed archetype, the concept of fog computing relocates the storage, computation, and services closer to the network’s edge, where the data is generated. Despite these advantages, the users expect proper load management in the fog environment. This has expanded the Internet of Things (IoT) field, increasing user requests for the fog computing layer. Given the growth, Virtual Machines (VMs) in the fog layer become overburdened due to user demands. In the fog layer, it is essential to evenly and fairly distribute the workload among the segment’s current VMs. Numerous load-management strategies for fog environments have been implemented up to this point. This study aims to create a hybridized and optimized approach for load management (HOGWO), in which the population set is generated using the Invasive Weed Optimisation (IWO) algorithm. The rest of the functional part is done with the help of the Grey Wolf Optimization (GWO) algorithm. This process ensures cost optimization, increased performance, scalability, and adaptability to any domain, such as healthcare, vehicular traffic management, etc. Also, the efficiency of the enhanced approach is analyzed in various scenarios to provide a more optimal solution set. The proposed approach is well illustrated and outperforms the existing algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), etc., in terms of cost and load management. It was found that more than 97% jobs were completed on time, according to the testing data, and the hybrid technique outperformed all other approaches in terms of fluctuation of load and makespan.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

This research received no specific grant from public, commercial, or not-for-profit funding agencies.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Centre for UG and PG Studies, BPUT, Rourkela, Odisha, 769015, India

    Debashreet Das

  2. Faculty of Natural, Mathematical and Engineering Sciences, King’s College London, Strand, London, WC2R 2LS, UK

    Sayak Sengupta

  3. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Vellore, Tamil Nadu, 632014, India

    Shashank Mouli Satapathy

  4. Machine Learning Engineer, Quantiphi Analytics, Bengaluru, Karnataka, 560048, India

    Deepanshu Saini

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  1. Debashreet Das
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  2. Sayak Sengupta
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Contributions

Debashreet Das: Writing-Conceptualization, Review, Validation, Supervision; Sayak Sengupta: Writing-original draft, Visualization, Software, Methodology, Data curation; Shashank Mouli Satapathy: Writing-review and editing, Validation, Supervision, Investigation, Formal analysis, Conceptualization; Deepanshu Saini: Writing-original draft, Visualization, Software, Methodology, Data curation.

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Correspondence to Shashank Mouli Satapathy.

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Das, D., Sengupta, S., Satapathy, S.M. et al. HOGWO: a fog inspired optimized load balancing approach using hybridized grey wolf algorithm. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04625-x

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