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 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.
Original language | English (US) |
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Article number | 8373684 |
Pages (from-to) | 302-317 |
Number of pages | 16 |
Journal | IEEE Transactions on Cloud Computing |
Volume | 9 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2021 |
Externally published | Yes |
Keywords
- Fisher market
- Market equilibrium
- algorithmic game theory
- edge computing
- fairness
- fog computing
ASJC Scopus subject areas
- Software
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications