Two-Stage Evolutionary Search for Efficient Task Offloading in Edge Computing Power Networks

In this article, we introduce the concept of edge computing power network (EdgeCPN) as a new paradigm to facilitate elastic integration and flexible scheduling of computing resources for task offloading in computing power networks (CPNs). Previous studies mainly focused on scheduling computing resources in the vertical dimension and may not effectively consider the computing resources selection in CPNs with increasingly diverse computing resources, which results in inefficient and unstable computing resource scheduling performance for task offloading. In this article, we design an on-demand computing resource scheduling model to enable efficient task offloading in EdgeCPNs. To improve the search efficiency and stability, we decouple the search for task offloading problems in EdgeCPNs into two stages and present a two-stage evolutionary search scheme (TESA). In stage-1, TESA first optimizes computing resources selection by searching a computing resources subset depending on the user budget, with the objective of maximizing the total gain. In stage-2, TESA jointly optimizes task offloading decisions and computing resources allocations based on the subset found in stage-1, with the objective of minimizing total delay. Numerical results confirm that the proposed scheme significantly enhances the efficiency and stability of the computing resources scheduling performance for task offloading in EdgeCPNs.