Price-based Resource Allocation for Edge Computing: A Market Equilibrium Approach

TL;DR

This paper introduces a market-based resource allocation framework for edge computing, using pricing and equilibrium concepts to optimize utilization, fairness, and profit maximization across heterogeneous edge nodes.

Contribution

It proposes a novel market equilibrium approach for edge resource allocation, including centralized and distributed algorithms, ensuring efficiency, fairness, and profit maximization.

Findings

Equilibrium solutions maximize resource utilization and fairness.

Distributed algorithms efficiently converge to market equilibrium.

Numerical results validate the effectiveness of the proposed methods.

Abstract

The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this work, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal (i.e., utility-maximizing) resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. drawn from the economics literature. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms are introduced, which efficiently converge to an ME. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques.