A New Task Offloading Strategy for Scheduling BoT Applications in a Mobile Edge Computing Environment

Abstract

This paper proposes a new metaheuristic algorithm for scheduling BoT applications, consisting of many independent tasks that can be processed in parallel, in a mobile edge computing environment under mobile device’s energy constraint. We formulate this energy-constrained BoT scheduling problem as an integer program. To solve the formulated optimization problem, we propose an efficient scheduling metaheuristic that employs the whale optimization algorithm (WOA) framework to determine the most appropriate task offloading solution. We use a task sequence to represent the offloading solution. The proposed algorithm starts with using the small-size-first policy to generate the initial solution, which is identified as the best individual in the initial whale population. We develop a cost-effective mapping operator to map each whale individual onto a feasible task sequence. We further design a greedy search-based task assignment strategy to determine the assignment of BoT tasks onto MEC servers under the energy constraint, such that the completion time of BoT applications can be determined and used as the fitness value of the corresponding whale individual. Built upon the WOA framework and DM operator, we develop the metaheuristic algorithm to iteratively update the population and explore better solutions. Experimental results show that the proposed scheduling algorithm outperforms the baseline algorithm in terms of both computational efficiency and scheduling performance when scheduling BoT applications in a MEC environment.