Abstract
Multiaccess edge computation (MEC) is a hotspot in 5G network. The problem of task offloading is one of the core problems in MEC. In this paper, a novel computation offloading model which partitions tasks into subtasksis proposed. This model takes communication and computing resources, energy consumption of intelligent mobile devices, and weight of tasks into account. We then transform the model into a multiobjective optimization problem based on Pareto that balances the task weight and time efficiency of the offloaded tasks. In addition, an algorithm based on hybrid immune and bat scheduling algorithm (HIBSA) is further designed to tackle the proposed multiobjective optimization problem. The experimental results show that HIBSA can meet the requirements of both the task execution deadline and the weight of the offloaded tasks.
Highlights
With the rapid development of the Internet of ings (IoT), intelligent mobile devices (IMDs) have become indispensable tools in people’s daily life, and their functions have become more and more powerful, which can meet people’s needs in social, shopping, travel, entertainment, and so on
We take the weight of weight priority greedy scheduling algorithm (WPGSA) as the benchmark and normalize its value to 1. us, the comparison of the results about the weight of each algorithm at 20 time slots is shown in Figure 6: Taking WPGSA as the benchmark, sequential scheduling algorithm (SSA), random scheduling algorithm (RSA), and time priority greedy scheduling algorithm (TPGSA) are not as good as WPGSA in weight, while hybrid immune and bat scheduling algorithm (HIBSA) proposed in this paper is better than WPGSA sometimes and slightly worse than WPGSA
Task offloading in mobile edge computing relieves the data computing pressure of local devices and central cloud by offloading data to the edge cloud, which reduces the task execution delay caused by the lack of computing resources
Summary
With the rapid development of the Internet of ings (IoT), intelligent mobile devices (IMDs) have become indispensable tools in people’s daily life, and their functions have become more and more powerful, which can meet people’s needs in social, shopping, travel, entertainment, and so on. Since mobile terminals can offload tasks to the nearby edge computing servers with rich computing resources, the problem of resource limitation of IMDs can be resolved to some extent. Many researchers have focused on the computation offloading scheduling problem Most of these studies have performance limitations, which can be explained from the following aspects. Application partitioning and repartitioning have been studied in depth in mobile cloud computing and distributed systems [11, 12], which can be used in the MEC system Following these two ideas, assigning tasks to multiple edge. (ii) We consider the scenario that the mobile device can generate multiple tasks at the same time, which is more realistic compared with most related works.
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