Mobility-Aware Offloading and Resource Allocation for Distributed Services Collaboration

In mobile edge computing (MEC) systems, mobile users (MUs) are capable of allocating local resources (CPU frequency and transmission power) and offloading tasks to edge servers in the vicinity in order to enhance their computation capabilities and reduce back-and-forth transmission over backhaul link. Nevertheless, mobile environment makes it hard to draw offloading and resource allocation decisions under dynamical wireless channel state and users' locations. In real life, social relationship is also provably a significant factor affecting integral performance in collaborative work, which results in MUs decisions strongly coupled and renders this problem further intractable. Most of previous works ignore the impact of inter-user dependency (or data dependency among IoT devices). To bridge this gap, we study the service collaboration with master-slave dependency among service chains of MUs and formulate this combinational optimization problem as a mixed integer non-linear programming (MINLP) problem. To this end, we derive the closed-form expression of resource allocation solution by convex optimization and transform it to integer linear programming (ILP) problem. Subsequently, we propose a distributed algorithm based on Markov approximation which has polynomial computation complexity. Experimental result on real-world dataset substantiates the usefulness and superiority of our scheme, in terms of reducing latency and energy consumption.

Automated summary

In mobile edge computing systems, users can offload tasks to nearby edge servers to improve computation capabilities and reduce transmission latency. However, the dynamic wireless channel state and user locations make it challenging to allocate resources and make offloading decisions. Additionally, the dependency among users also has a significant impact on collaborative work. In this study, we propose a mathematical optimization problem to address this issue and develop a distributed algorithm based on Markov approximation. Experimental results demonstrate the effectiveness of our scheme in reducing latency and energy consumption.