CPU Scheduling in Data Centers Using Asynchronous Finite-Time Distributed Coordination Mechanisms

TL;DR

This paper introduces an asynchronous finite-time distributed coordination algorithm for CPU scheduling in data centers, enabling efficient, robust, and near-optimal task allocation despite communication delays and node variability.

Contribution

It presents a novel asynchronous finite-time convergence scheme for distributed scheduling, improving robustness and efficiency in data center resource management.

Findings

Achieves finite-time convergence to near-optimal schedules

Robust against communication delays and straggler nodes

Demonstrates state-of-the-art performance in simulations

Abstract

We propose an asynchronous iterative scheme that allows a set of interconnected nodes to distributively reach an agreement within a pre-specified bound in a finite number of steps. While this scheme could be adopted in a wide variety of applications, we discuss it within the context of task scheduling for data centers. In this context, the algorithm is guaranteed to approximately converge to the optimal scheduling plan, given the available resources, in a finite number of steps. Furthermore, by being asynchronous, the proposed scheme is able to take into account the uncertainty that can be introduced from straggler nodes or communication issues in the form of latency variability while still converging to the target objective. In addition, by using extensive empirical evaluation through simulations we show that the proposed method exhibits state-of-the-art performance.