Local ratio based real-time job offloading and resource allocation in mobile edge computing

Abstract

Mobile Edge Computing (MEC) has emerged as a promising paradigm enabling vehicles to handle computation-intensive and time-sensitive applications for intelligent transportation. Due to the limited resources in MEC, effective resource management is crucial for improving system performance. While existing studies mostly focus on the job offloading problem and assume that job resource demands are fixed and given apriori, the joint consideration of job offloading (selecting the edge server for each job) and resource allocation (determining the bandwidth and computation resources for offloading and processing) remains underexplored. This paper addresses the joint problem for deadline-constrained jobs in MEC with both communication and computation resource constraints, aiming to maximize the total utility gained from jobs. This problem is equivalent to a 2-dimensional multiple-choice generalized assignment problem (2DMCGAP). To tackle this problem, we propose an approximation algorithm, $\mathtt{IDAssign}$, with an approximation bound of $\frac{1}{6}$. $\mathtt{IDAssign}$ is the first approximation solution with constant approximation ratio for our problem as well as for 2DMCGAP. Finally, we experimentally evaluate the performance of $\mathtt{IDAssign}$ and compare it to state-of-the-art heuristics using a real-world taxi trace and object detection applications.