As the Mobile Edge Computing(MEC)environment faces increasing user demands,the problems of delay and energy consumption stemming from the limited computing and storage resources of mobile devices are becoming increasingly prominent.These issues are further exacerbated by the repeated offloading and processing of tasks in mobile devices.To minimize the delay and energy consumption of task offloading,an MEC task offloading scheme with a caching mechanism is proposed herein.Firstly,a cache content selection model is established based on factors such as task popularity,freshness,and data size,and cache update strategies are formulated according to the results obtained.Then,a joint optimization model is established to address the issues of task offloading and caching,aiming to minimize the total system cost by considering the impact of task offloading and caching on device delay and energy consumption.To solve this complex optimization problem,the objective function is constrained by penalty functions and solved using the Particle Swarm Optimization(PSO)algorithm.The experimental results show that compared with traditional methods such as LOCal computing without caching(LOC),task OFFloading without caching(OFF),and task offloading using random caching,the total delay of this scheme is reduced by more than 37.00%while the cache hit rate is increased by more than 7.78%,indicating a high utilization rate of cache resources.
Mobile Edge Computing(MEC)task offloadingcaching mechanismcache updatecache hit rate
国家科技期刊平台
NSTL
万方数据
