Dependency-Driven Computation Completion Time Minimization for MEC Networks

In response to the escalating data volumes and the pressing need for reduced network latency in Mobile Edge Computing (MEC), this paper delves into the sphere of edge computing. Many MEC platforms are turning to container-based virtualization and leveraging image layering to cut down transmission costs. Amid this shift, applications are growing more complex, composed of multiple tasks and demanding diverse execution environments. However, existing research has mainly concentrated on task scheduling within edge systems, sidelining the vital aspect of preparing runtime environments on MEC servers. To bridge this gap, our paper tackles task scheduling complexities, including data and layer dependencies. It offers an integrated solution that optimizes task scheduling and layer loading across MEC servers, all with the goal of minimizing the total computation completion time. To address this NP-hard problem, we present a heuristic task-scheduling algorithm rooted in the genetic algorithm (GA) and complement it with an in-depth exploration of layer-loading policies. Our experiments conclusively demonstrate the substantial reduction in total computation completion time achievable through this approach.