Optimal CPU Scheduling in Data Centers via a Finite-Time Distributed Quantized Coordination Mechanism

TL;DR

This paper introduces a finite-time distributed algorithm for optimal task scheduling in data centers that uses quantized communication and event-driven updates, ensuring exact convergence and distributed stopping in large-scale networks.

Contribution

The paper presents a novel finite-time distributed scheduling algorithm that guarantees exact optimality, incorporates quantized data exchange, and enables distributed termination detection.

Findings

Algorithm converges to the optimal schedule in finite steps

Operates effectively with quantized information

Demonstrates state-of-the-art performance in simulations

Abstract

In this paper we analyze the problem of optimal task scheduling for data centers. Given the available resources and tasks, we propose a fast distributed iterative algorithm which operates over a large scale network of nodes and allows each of the interconnected nodes to reach agreement to an optimal solution in a finite number of time steps. More specifically, the algorithm (i) is guaranteed to converge to the exact optimal scheduling plan in a finite number of time steps and, (ii) once the goal of task scheduling is achieved, it exhibits distributed stopping capabilities (i.e., it allows the nodes to distributely determine whether they can terminate the operation of the algorithm). Furthermore, the proposed algorithm operates exclusively with quantized values (i.e., the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We also provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithm. Finally, by using extensive empirical evaluations through simulations we show that the proposed algorithm exhibits state-of-the-art performance.