Cost-effective Service Deployment and Balanced Traffic Management on Edge

The multi-access edge computing (MEC) technologies have advanced rapidly, bringing the 5G network vision, particularly massive machine type communication (mMTC), closer to people. Computing tasks are offloaded to a widely distributed network edge cluster, enabling efficient and real-time sensing and interaction for mobile devices. However, limited computation and communication resources in edge devices require caution in service deployment and traffic management to maintain overall load balancing, especially during heavy network loads. We explore the performance-cost relationship and transform the optimization problem into a nonlinear integer programming problem (NIP). Our genetic algorithm-based approach, GA4CBST, outperforms baselines in efficiency and effectiveness.