MDT-NJUST dataset for studying mobility-aware task offloading/scheduling problems in MEC environments

When designing task scheduling algorithms in mobile edge computing (MEC), the mobile device (MD)'s mobility becomes an important concern, since the change in MD's location would affect the data transmission rate, leading to fluctuations in task transmission duration and completion time. In this paper, we study a mobility-aware task off-and-downloading scheduling problem in MEC, considering both the communication delay and energy consumption caused by the data offloading and the result downloading. We first formulate a mathematical optimization model of the considered problem and prove its NP-hardness. To explore high-quality task scheduling decisions, we propose a swarm intelligence algorithm-based evolutionary computation (START) framework. The main technological innovations of START include a solution representation, an exponential probability model-based mapping operator, and a problem-dependent fitness evaluation method. Specifically, the solution representation makes START applicable to a wide range of swarm intelligence algorithms. The mapping operator links the individual space and solution space, whose critical parameter is set based on rigorous theoretical analysis. The fitness evaluation method is the only component of the START framework that is relevant to the particular problem, providing the extensibility of applying START to solving other problems. In experiments, we create a real-world MD trajectory dataset MDT-NJUST, and integrate several widely-used swarm intelligence algorithms to justify START's performance in terms of effectiveness and efficiency. Experimental results also verified the conclusion drawn from the theoretical analysis on critical parameter determination.