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A metaheuristic-based computation offloading in edge-cloud environment

Abstract

In recent years, mobile applications have emerged as a conceivable solution to facilitate daily activities in various aspects of human life. Due to the resource-limited of mobile devices, they are inadequate to execute mobile applications. To deal with this issue, edge clouds have emerged to extend resource capabilities at the network edge near mobile devices. Therefore, transferring and outsourcing compute-intensive tasks from mobile devices to edge servers is one of the challenging issues to be investigated. This paper considers the task offloading issue as an NP-hard problem and proposes a metaheuristic-based task offloading mechanism using the non-dominated sorting genetic algorithm (NSGA-II) technique named iNSGA-II for serving mobile applications in the edge/cloud networks. Besides, we improve the crossover and mutation operators, making the proposed solution converge faster than other evolutionary algorithms. The obtained numerical results under synthetic workloads indicate that the proposed mechanism is a cost-effective solution, and it increases the average edge server utilization and reduces the energy consumption and the execution time than metaheuristic-based task offloading mechanisms.

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Acknowledgements

The authors would like to thank the Islamic Azad University of Qom Branch for supporting this paper under the research project titled “Autonomic Computation Offloading in Edge Computing”.

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Correspondence to Mostafa Ghobaei-Arani.

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Shahidinejad, A., Ghobaei-Arani, M. A metaheuristic-based computation offloading in edge-cloud environment. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03561-7

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Keywords

  • Edge cloud computing
  • Task offloading
  • IoT applications
  • iNSGA-II