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Energy- and Cost-Aware Scheduling for Task- Dependency Applications in Mobile Edge Computing

Mobile edge computing (MEC) is a promising way to improve the application performance on mobile devices in recent years, especially in terms of energy saving. However, users have to pay additional service costs such as communication costs when using services provided by MEC. In this paper, we manage to minimize both the average energy consumption and the average communication cost of mobile devices in a mobile edge computing environment with multiple users. We consider a MEC system that consists of multiple cloudlets and a cloud. Users submit their applications consisting of tasks with dependencies to this MEC system. We combine computation offloading and dynamic voltage frequency scaling (DVFS) technologies to reduce the power consumption of mobile devices. We formulate this problem into a mixed-integer nonlinear programming model and propose an adaptive multiobjective evolutionary algorithm based on Non-dominated Sorting Genetic Algorithm III (NSGA-III) to solve it. Simulation experiments are conducted to compare the proposed algorithm with three baseline algorithms and two multi-objective optimization algorithms, which validates the superiority of the proposed algorithms.