Cloud-Edge collaborative task offloading model using improved biogeography-based optimization algorithm

In the cloud-edge collaborative task scheduling framework, when the task volume on the edge node reaches a certain threshold, the edge node becomes unable to effectively migrate additional tasks. To solve the problem of ineffective optimization in task migration on the edge node, this paper proposes a task offloading strategy. Based on the Biogeography-based Optimization (BBO) algorithm, modifications are introduced to its initialization population strategy, migration method, and mutation method, enabling it to better address the multi-objective optimization problem of jointly minimizing delay and energy consumption. The leakage detection of pipelines is used to establish a simulation model, and experimentally verified that the algorithm can be more targeted to solve the scheduling in the unloading process of cloud-edge collaborative task offloading