AIMD scheduling and resource allocation in distributed computing systems

TL;DR

This paper introduces a stable AIMD-based scheduling and resource allocation method for distributed computing systems, ensuring convergence and QoS guarantees through decentralized control and event-driven modeling.

Contribution

It presents a novel AIMD-like admission control policy and a decentralized resource allocation strategy with proven stability and convergence in distributed systems.

Findings

AIMD policy is stable regardless of system size and parameters.

The decentralized controller guarantees finite-time convergence.

The approach aligns with QoS specifications via Little's Law.

Abstract

We consider the problem of simultaneous scheduling and resource allocation of an incoming flow of requests to a set of computing units. By representing each computing unit as a node, we model the overall system as a multi-queue scheme. Inspired by congestion control approaches in communication networks, we propose an AIMD-like (additive increase multiplicative decrease) admission control policy that is stable irrespective of the total number of nodes and AIMD parameters. The admission policy allows us to establish an event-driven discrete model, triggered by a locally identifiable enabling condition. Subsequently, we propose a decentralized resource allocation strategy via a simple nonlinear state feedback controller, guaranteeing global convergence to a bounded set in finite time. Last, we reveal the connection of these properties with Quality of Service specifications, by calculating local queuing time via a simple formula consistent with Little's Law.