Design and Implementation of Distributed Resource Management for Time Sensitive Applications

TL;DR

This paper introduces a distributed resource management framework for time-sensitive applications that ensures fair CPU allocation through recursive updates, demonstrating robustness and adaptability with minimal information requirements.

Contribution

It presents a novel distributed approach for fair CPU resource allocation that decomposes a centralized objective into recursive, adaptive processes for applications and resource managers.

Findings

Framework converges to fair allocations under specified conditions.

System adapts to changes in application number and types.

Validated through MATLAB/Simulink simulations.

Abstract

In this paper, we address distributed convergence to fair allocations of CPU resources for time-sensitive applications. We propose a novel resource management framework where a centralized objective for fair allocations is decomposed into a pair of performance-driven recursive processes for updating: (a) the allocation of computing bandwidth to the applications (resource adaptation), executed by the resource manager, and (b) the service level of each application (service-level adaptation), executed by each application independently. We provide conditions under which the distributed recursive scheme exhibits convergence to solutions of the centralized objective (i.e., fair allocations). Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. We finally validate our framework with simulations using the TrueTime toolbox in MATLAB/Simulink.