Task migration computation offloading with low delay for mobile edge computing in vehicular networks

摘要

Nowadays, a new paradigm named mobile edge computing (MEC) is capable of supplying some cloud-like functions at the edges of wireless networks, which enables vehicles to offload the computation intensive tasks on MEC servers with low latency. However, new challenges posed by the complex network environment and the mobility of vehicles are usually not covered by traditional offloading schemes. To solve such problems, we propose a heuristic task migration computation offloading (TMCO) scheme. Compared with traditional ones, TMCO can dynamically choose suitable places to offload the tasks for moving vehicles within deadline. For this purpose, the mobility of vehicle and strict delay deadline are considered comprehensively. We use hash table to store the number of tasks on the corresponding server and use random function to simulate the probability of task offloading. In terms of latency, experimental results suggest that the performance of TMCO is on average 10% higher than that of traditional full offloading schemes.