Energy-aware allocation for delay-sensitive multitask in mobile edge computing

With lower network latency and powerful hardware, mobile edge computing (MEC) is effective for computation-intensive and delay-sensitive tasks. The rising energy and low-latency demands of mobile applications for MEC pose challenges to task allocation. In this work, we consider computation offloading in MEC that is composed of a set of mobile devices, each with multiple tasks to offload to a nearby MEC server, with both single and multiple access points (APs). With multiple APs deployed, each mobile device can directly communicate with one or more APs, and their tasks can offload to different MEC servers that are within direct communication range. By considering binary computation offloading mode and limited subchannels, we formulate the multitask allocation problem as an integer programming problem, with the objective of minimizing the total energy consumption of all mobile devices while meeting deadline requirements. To solve this complicated problem, we propose efficient algorithms for single and multiple APs, and analyze related properties, including the approximation ratio and complexity. Experiments show that the algorithm can find high-quality solutions in a short time.