Energy-efficient offloading based on hybrid bio-inspired algorithm for edge–cloud integrated computation

Abstract

Mobile Edge Computing (MEC) is deployed closer to User Equipment (UE) and has strong computing power. Not only it relieves the load pressure on the central cloud, but also effectively reduces the transmission delay caused by offloading computation from devices because it is closer to users. Therefore, we study edge computing task offloading based on edge–cloud collaboration scenarios to meet the requirement of low delay and high energy efficiency. In order to improve the convergence accuracy and system energy efficiency, we proposed a hybrid bio-inspired algorithm, the HS-HHO algorithm, which combines the Slime Mode Algorithm (SMA) and the optimized Harris Hawks Optimizer (HHO). For different types of tasks, we design a task clustering scheme based on K-medoids clustering for edge cloud scenarios, which clusters tasks into computation-intensive, data-intensive, and integrated, and is used to optimize the offloading objectives of each type of tasks. Experimental results demonstrate that our proposed HS-HHO algorithm takes into account the time delay while effectively reducing energy consumption and making full use of the computational resources. The HS-HHO algorithm improves the total energy efficiency of the system by about 22% compared with the SMA, HHO, and AO algorithm strategies.