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

arXiv: https://arxiv.org/abs/2104.03126 · HTML: https://mlsanity.com/paper/2104.03126/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [Discretized Distributed Optimization over Dynamic Digraphs](https://mlsanity.com/paper/2311.07939/) (2023)
- [Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks](https://mlsanity.com/paper/2410.20345/) (2024)
- [Distributed Finite-Sum Constrained Optimization subject to Nonlinearity on the Node Dynamics](https://mlsanity.com/paper/2203.14527/) (2022)
- [Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers](https://mlsanity.com/paper/2510.25176/) (2025)

## Abstract (arXiv)

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.