Derandomized Distributed Multi-resource Allocation with Little Communication Overhead

TL;DR

This paper introduces a derandomized AIMD algorithm for distributed multi-resource allocation that requires minimal communication, preserves privacy, and converges faster than stochastic methods while achieving near-optimal allocations.

Contribution

It presents a novel derandomized AIMD algorithm that operates with one-bit feedback, no inter-device communication, and faster convergence for shared resource allocation.

Findings

Converges to optimal resource allocations

Achieves minimum social cost

Faster convergence than stochastic AIMD

Abstract

We study a class of distributed optimization problems for multiple shared resource allocation in Internet-connected devices. We propose a derandomized version of an existing stochastic additive-increase and multiplicative-decrease (AIMD) algorithm. The proposed solution uses one bit feedback signal for each resource between the system and the Internet-connected devices and does not require inter-device communication. Additionally, the Internet-connected devices do not compromise their privacy and the solution does not dependent on the number of participating devices. In the system, each Internet-connected device has private cost functions which are strictly convex, twice continuously differentiable and increasing. We show empirically that the long-term average allocations of multiple shared resources converge to optimal allocations and the system achieves minimum social cost. Furthermore, we show that the proposed derandomized AIMD algorithm converges faster than the stochastic AIMD algorithm and both the approaches provide approximately same solutions.