A Computation Offloading Scheme Based on FFA and GA for Time and Energy Consumption

With the development of Internet of Things (IoT) technology, the types and the volume of business have been increasing rapidly. The existing centralized cloud processing model is hard to meet the requirements of delay-sensitive and compute-intensive services. So, mobile edge computing and cloud computing are introduced to realize a cloud-edge-terminal collaboration network architecture. However, there still exist problems such as high energy consumption and long delay among devices and servers. To overcome these challenges, a cloud-edge-terminal collaboration offloading scheme based on first fit algorithm (FFA) and genetic algorithm (GA) is proposed, which combines two allocation modes. On one hand, for delay-sensitive tasks, FFA is designed to quickly offload tasks. On the other hand, for dense tasks, GA is designed to accurately offload tasks. To make the best of the advantages and avoid the disadvantages of FFA and GA, we adopt the method of using two algorithms alternately, and restrict the rules of the alternations. At the end, the characteristics of the algorithm are studied by simulation and compared with other algorithms to verify the performance of the algorithm.