Truthful Computation Offloading Mechanisms for Edge Computing
Weibin Ma, Lena Mashayekhy

TL;DR
This paper introduces a truthful mechanism for computation offloading in edge computing that optimizes resource allocation, ensuring users report preferences honestly and improving latency performance.
Contribution
It presents a novel algorithmic mechanism design approach for truthful computation offloading, addressing preference misreporting and resource optimization in edge computing.
Findings
Mechanism ensures truthful preference reporting as a weakly dominant strategy.
Outperforms existing strategies in reducing end-to-end latency.
Achieves system equilibrium with optimized resource allocation.
Abstract
Edge computing (EC) is a promising paradigm providing a distributed computing solution for users at the edge of the network. Preserving satisfactory quality of experience (QoE) for users when offloading their computation to EC is a non-trivial problem. Computation offloading in EC requires jointly optimizing access points (APs) allocation and edge service placement for users, which is computationally intractable due to its combinatorial nature. Moreover, users are self-interested, and they can misreport their preferences leading to inefficient resource allocation and network congestion. In this paper, we tackle this problem and design a novel mechanism based on algorithmic mechanism design to implement a system equilibrium. Our mechanism assigns a proper pair of AP and edge server along with a service price for each new joining user maximizing the instant social surplus while satisfying…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIoT and Edge/Fog Computing · Mobile Crowdsensing and Crowdsourcing · Blockchain Technology Applications and Security
