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

The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (loT) applications. In this paper, 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 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. 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, which efficiently converge to an ME, are introduced. 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.

Automated summary

The paper introduces a market-based framework for efficiently allocating resources among heterogeneous edge nodes to multiple services at the network edge. The framework generates a market equilibrium solution by properly pricing the nodes and aims to maximize resource utilization while considering budget constraints. The study shows that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties.