A Multi-User Effective Computation Offloading Mechanism for MEC System: Batched Multi-Armed Bandits Approach

TL;DR

This paper introduces a batch-based multi-user server elimination algorithm for MEC systems that reduces decision time and improves offloading efficiency by grouping users and limiting server choices.

Contribution

It proposes a novel BMSE algorithm with user-level and system-level sub-algorithms to enhance multi-user task offloading in MEC, addressing scalability issues.

Findings

Reduces server selection time significantly.

Achieves sub-linear regret convergence.

Demonstrates improved offloading performance in experiments.

Abstract

With the development of 5G technology, mobile edge computing (MEC) is becoming a useful architecture, which is envisioned as a cloud computing extension version. Users within MEC system could deal with data processing at edge terminals, which can reduce time for communication or data transmission. Multi-armed bandits (MAB) algorithms are powerful tools helping users offloading tasks to their best servers in MEC. However, as the number of users and tasks growing, the frequency of selecting servers and the cost of making decision is growing rapidly under traditional MAB algorithms. Inspired by this, in this paper, we propose a Batch-based Multi-user Server Elimination (BMSE) algorithm to solve such problem, which includes two sub-algorithms. We firstly propose a sub-algorithm in user level (BMSE-UL) to reduce the time cost. In BMSE-UL, users can simplify its own available server groups and offload tasks collectively. Then another sub-algorithm in system level (BMSE-SL) is proposed to reduce the frequency of making decision. In BMSE-SL, the system can cut down all the suboptimal task offloading actions and make the choosing option unique. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness of BMSE by extensive experiments.