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

• Published in: IEEE transactions on cloud computing Vol. 9; no. 1; pp. 302 - 317
• Main Authors: Nguyen, Duong Tung, Le, Long Bao, Bhargava, Vijay
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE Computer Society 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: algorithmic game theory; Algorithms; Cloud computing; Computational geometry; Computational modeling; Convexity; Delays; Edge computing; fairness; Fisher market; fog computing; Geographical distribution; Internet of Things; Market equilibrium; Markets; Optimization; Reliability; Resource allocation; Resource management; Resource utilization; Revenue; Security; User experience
• ISSN: 2168-7161; 2372-0018
• DOI: 10.1109/TCC.2018.2844379

The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) 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.