Journal
IEEE TRANSACTIONS ON CLOUD COMPUTING
Volume 9, Issue 1, Pages 302-317Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCC.2018.2844379
Keywords
Market equilibrium; Fisher market; fairness; algorithmic game theory; edge computing; fog computing
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
- Vanier Graduate Scholarship
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available