A Dichotomous Repair-Based Load-Balanced Task Allocation Strategy in Cloud-Edge Environment

Load balancing is a hot issue in the current cloud-edge environment. However, due to the characteristics of edge computing, load balancing needs to be better integrated with edge devices and edge networks to provide higher performance and reliability. The presence of a large number of overloaded nodes may lead to load imbalance and thus affect the efficiency of nodes. To solve this problem, the key is how to allocate tasks to the appropriate resources. To this end, this work proposes a dichotomous task allocation policy Dichotomous Repair (DREP) to achieve efficient task allocation and overall load balancing of edge nodes in cloud-edge environment. The proposed policy consists of five steps: grouping, adjustment, filtering, greed and repair. The dichotomous policy is adopted to generate the initial allocation scheme according to the number of edge nodes, and then the overloaded and underloaded nodes are repaired by the subsequent two-stage repair policy to maintain the load balance. Finally, through extensive experiments, we evaluate the proposed method and the results show that it outperforms other algorithms in terms of workload balancing.