Towards Effective Resource Procurement in MEC: A Resource Re-Selling Framework | IEEE Journals & Magazine | IEEE Xplore

Towards Effective Resource Procurement in MEC: A Resource Re-Selling Framework


Abstract:

On-demand and resource reservation pricing models, widely used in cloud computing, are currently used in Multi-Access Edge Computing (MEC). Nevertheless the edge's resour...Show More

Abstract:

On-demand and resource reservation pricing models, widely used in cloud computing, are currently used in Multi-Access Edge Computing (MEC). Nevertheless the edge's resources are distributed and each server has lower capacity. If too much resources were reserved in advance, on-demand users may not get their jobs served on time, jeopardizing MEC's latency benefits. Concurrently, reservation plan users may possess un-used quota. Therefore, we propose a sharing platform where reservation plan users can re-sell unused resource quota to on-demand users. To investigate the mobile network operator's (MNO‘s) incentive of allowing re-selling, we formulate a 3-stage non-cooperative Stackelberg Game and characterize the optimal strategies of buyers and re-sellers. We show that users’ actions give rise to 4 different outcomes at equilibrium, dependent on the prices and supply levels of the sharing and on-demand pools. Based on the 4 possible outcomes, we characterise the MNO's optimal prices for on-demand users. Numerical results show that having both pools gives the MNO an optimal revenue when the on-demand pool's supply is low, and unexpectedly, when the MNO's commission is low. We develop an interactive prototype, and show that users’ decision distributions in studies on our prototype are similar to that of our decision model.
Published in: IEEE Transactions on Services Computing ( Volume: 17, Issue: 1, Jan.-Feb. 2024)
Page(s): 82 - 97
Date of Publication: 20 November 2023

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I. Introduction

Edge computing enables a wide variety of low latency and computationally intensive services on mobile and other resource constrained devices (e.g Internet of Things (IoT) devices). These low latency services include video analytics, real-time analytics, virtual and augmented reality (VR/AR), and connected vehicle decision making. Edge computing brings the power of cloud computing to the network edge, with servers placed at edge access points e.g. base stations or wifi access points [1]. Users and device owners can offload computationally intensive tasks to the nearby edge servers, and receive them within latency requirements [2], due to the close proximity of the edge servers. The wide-area-network (WAN) delay of cloud computing will be avoided [3].

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References

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