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An Opposition-Based Hybrid Evolutionary Approach for Task Scheduling in Fog Computing Network

  • Research Article-Computer Engineering and Computer Science
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Abstract

The services generated by IoT devices have increased with the increasing demand and capabilities of IoT applications. Proximal fog computing networks efficiently facilitate the latency-sensitive services generated by these devices. A better scheduling technique can effectively minimize the service time of devices. Scheduling using an evolutionary method has been proved to be a better choice for service-time minimization. OBCR (opposition-based chemical reaction) method is developed for task scheduling in fog network by utilizing the features of heuristic upward ranking technique and CRO (chemical reaction optimization) technique with OBL (opposition-based learning). The use of OBL in OBCR results in a more diverse population and aids in escaping the local optima. OBCR uses four operators to provide better exploration and exploitation of the solution space. Furthermore, OBCR also improves the stability of these dynamic fog devices. Extensive simulations have demonstrated the better performance of the proposed OBCR technique compared to other techniques in terms of service-time latency and stability. It also has verified the importance of the technique for fog computing networks.

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Authors and Affiliations

  1. IIT Roorkee, Roorkee, India

    Ashish Mohan Yadav, Kuldeep Narayan Tripathi & S. C. Sharma

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  1. Ashish Mohan Yadav
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  2. Kuldeep Narayan Tripathi
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Correspondence to Ashish Mohan Yadav.

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Yadav, A.M., Tripathi, K.N. & Sharma, S.C. An Opposition-Based Hybrid Evolutionary Approach for Task Scheduling in Fog Computing Network. Arab J Sci Eng 48, 1547–1562 (2023). https://doi.org/10.1007/s13369-022-06918-y

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