MoDEMS: Optimizing Edge Computing Migrations for User Mobility

Edge computing capabilities in 5G wireless networks promise to benefit mobile users: computing tasks can be offloaded from user devices to nearby edge servers, reducing users' experienced latencies. Few works have addressed how this offloading should handle long-term user mobility: as devices move, they will need to offload to different edge servers, which may require migrating data or state information from one edge server to another. In this paper, we introduce MoDEMS, a system model and architecture that provides a rigorous theoretical framework and studies the challenges of such migrations to minimize the service provider cost and user latency. We show that this cost minimization problem can be expressed as an integer linear programming problem, which is hard to solve due to resource constraints at the servers and unknown user mobility patterns. We show that finding the optimal migration plan is in general NP-hard, and we propose alternative heuristic solution algorithms that perform well in both theory and practice. We finally validate our results with real user mobility traces, ns-3 simulations, and an LTE testbed experiment. Migrations reduce the latency experienced by users of edge applications by 33% compared to previously proposed migration approaches.

Automated summary

Edge computing capabilities in 5G wireless networks can reduce latency for mobile users by offloading computing tasks from user devices to nearby edge servers. This paper introduces MoDEMS, a system model and architecture that addresses the challenges of handling long-term user mobility in offloading, aiming to minimize service provider cost and user latency. The authors propose alternative heuristic algorithms for solving the cost minimization problem and validate the results through various experiments, showing a 33% reduction in latency compared to previous migration approaches.