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  • 1.
    Yin, Lu
    et al.
    School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, China.
    Sun, Jin
    School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, China.
    Zhou, Junlong
    School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing, China.
    Gu, Zonghua
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Li, Keqin
    Department of Computer Science, State University of New York, New Paltz, NY, USA.
    ECFA: an efficient convergent firefly algorithm for solving task scheduling problems in cloud-edge computing2023In: IEEE Transactions on Services Computing, E-ISSN 1939-1374, p. 1-14Article in journal (Refereed)
    Abstract [en]

    In cloud-edge computing paradigms, the integration of edge servers and task offloading mechanisms has posed new challenges to developing task scheduling strategies. This paper proposes an efficient convergent firefly algorithm (ECFA) for scheduling security-critical tasks onto edge servers and the cloud datacenter. The proposed ECFA uses a probability-based mapping operator to convert an individual firefly into a scheduling solution, in order to associate the firefly space with the solution space. Distinct from the standard FA, ECFA employs a low-complexity position update strategy to enhance computational efficiency in solution exploration. In addition, we provide a rigorous theoretical analysis to justify that ECFA owns the capability of converging to the global best individual in the firefly space. Furthermore, we introduce the concept of boundary traps for analyzing firefly movement trajectories, and investigate whether ECFA would fall into boundary traps during the evolutionary procedure under different parameter settings. We create various testing instances to evaluate the performance of ECFA in solving the cloud-edge scheduling problem, demonstrating its superiority over FA-based and other competing metaheuristics. Evaluation results also validate that the parameter range derived from the theoretical analysis can prevent our algorithm from falling into boundary traps.

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