Computing Offloading Strategy Based on Improved Firework Algorithm

Abstract

AbstractThe emergence of mobile edge computing greatly alleviates the problem of insufficient computing power of mobile devices and does not support high-energy applications. As an important part of edge computing, computing offloading can greatly improve the quality of service through a reasonable computing offload scheme. For time delay sensitive tasks, the time delay of computational offloading under energy consumption constraints is too large, this paper introduces an improved fireworks algorithm based on grouping and classification (GCFA). The problem is modeled as the minimum delay problem under the constraint of energy consumption, and the offloading vector is calculated by GCFA, which transforms the task offloading into the process of fireworks particle optimization. Finally, through experiments, the genetic algorithm (GA) offloading strategy, standard fireworks algorithm (FA) offloading strategy, bat algorithm (BA) offloading strategy and mayfly optimization algorithm (MA) are compared, the average total system cost of GCFA is much lower than that of the other four. The total system cost of GCFA is 20% lower than that of the original. The experimental results show that GCFA has a good performance in reducing MEC time delay and balancing the load of MEC server.KeywordsEdge computingComputing offloadingFirework algorithm