Task Offloading and Resource Allocation with Multiple CAPs and Selfish Users
Eric Jiang, Meng-Hsi Chen, Ben Liang, and Min Dong

TL;DR
This paper addresses task offloading and resource allocation in multi-user mobile edge computing with multiple CAPs, proposing a centralized heuristic and a game-theoretic approach to achieve near-optimal solutions considering selfish user behavior.
Contribution
It introduces MCAP and MCAP-NE algorithms, combining SDP relaxation and game theory to optimize offloading and resource allocation in multi-CAP MEC systems.
Findings
NE solution achieves near-optimal system cost
MCAP-NE reduces computation time significantly
Proposed methods outperform random mapping and approach optimal performance
Abstract
In this work, we consider a multi-user mobile edge computing system with multiple computing access points (CAPs). Each mobile user has multiple dependent tasks that must be processed in a round-by-round schedule. In every round, a user may process their individual task locally, or choose to offload their task to one of the CAPs or the remote cloud server, in order to possibly reduce their processing cost. We aim to jointly optimize the offloading decisions of the users and the resource allocation decisions for each CAP over a global objective function, defined as a weighted sum of total energy consumption and the round time. We first present a centralized heuristic solution, termed MCAP, where the original problem is relaxed to a semi-definite program (SDP) to probabilistically generate the offloading decision. Then, recognizing that the users often exhibit selfish behavior to…
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Taxonomy
TopicsIoT and Edge/Fog Computing · Cloud Computing and Resource Management · Age of Information Optimization
