Distributed and Efficient Resource Balancing Among Many Suppliers and Consumers
Kamal Chaturvedi, Jia Yuan Yu, Shrisha Rao

TL;DR
This paper presents a distributed, communication-free algorithm based on AIMD for balancing supply and demand among self-interested agents, converging to social optimality in multi-agent markets.
Contribution
It introduces a novel AIMD-based probabilistic algorithm ensuring convergence to social optimum without inter-agent communication.
Findings
Almost sure convergence to social optimum.
Effective profit maximization for individual agents.
Simulation results confirm algorithm efficacy.
Abstract
Achieving a balance of supply and demand in a multi-agent system with many individual self-interested and rational agents that act as suppliers and consumers is a natural problem in a variety of real-life domains---smart power grids, data centers, and others. In this paper, we address the profit-maximization problem for a group of distributed supplier and consumer agents, with no inter-agent communication. We simulate a scenario of a market with suppliers and consumers such that at every instant, each supplier agent supplies a certain quantity and simultaneously, each consumer agent consumes a certain quantity. The information about the total amount supplied and consumed is only kept with the center. The proposed algorithm is a combination of the classical additive-increase multiplicative-decrease (AIMD) algorithm in conjunction with a probabilistic rule for the agents to…
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Taxonomy
TopicsAdvanced Queuing Theory Analysis · Optimization and Search Problems · Network Traffic and Congestion Control
