# Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers

arXiv: https://arxiv.org/abs/2510.25176 · HTML: https://mlsanity.com/paper/2510.25176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 61 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization](https://mlsanity.com/paper/1806.08054/) (2018)
- [Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions](https://mlsanity.com/paper/1811.03982/) (2018)
- [ADD-OPT: Accelerated Distributed Directed Optimization](https://mlsanity.com/paper/1607.04757/) (2016)
- [A fast randomized incremental gradient method for decentralized non-convex optimization](https://mlsanity.com/paper/2011.03853/) (2020)
- [S-ADDOPT: Decentralized stochastic first-order optimization over directed graphs](https://mlsanity.com/paper/2005.07785/) (2020)
- [Quantizing data for distributed learning](https://mlsanity.com/paper/2012.07913/) (2020)
- [Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphs](https://mlsanity.com/paper/2008.06082/) (2020)
- [Quantized Consensus ADMM for Multi-Agent Distributed Optimization](https://mlsanity.com/paper/1510.08736/) (2015)
- [Consensus-Based Distributed Estimation in the Presence of Heterogeneous, Time-Invariant Delays](https://mlsanity.com/paper/2104.00394/) (2021)
- [Discretized Distributed Optimization over Dynamic Digraphs](https://mlsanity.com/paper/2311.07939/) (2023)
- [Optimal CPU Scheduling in Data Centers via a Finite-Time Distributed Quantized Coordination Mechanism](https://mlsanity.com/paper/2104.03126/) (2021)
- [Accelerated Distributed Allocation](https://mlsanity.com/paper/2401.15598/) (2024)
- [Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation](https://mlsanity.com/paper/2310.18225/) (2023)
- [Top-k data selection via distributed sample quantile inference](https://mlsanity.com/paper/2212.00230/) (2022)
- [Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond](https://mlsanity.com/paper/2308.11263/) (2023)
- [Energy Efficient Distributed Processing for IoT](https://mlsanity.com/paper/2001.02974/) (2020)
- [CPU Scheduling in Data Centers Using Asynchronous Finite-Time Distributed Coordination Mechanisms](https://mlsanity.com/paper/2101.06139/) (2021)
- [Online Distributed Learning with Quantized Finite-Time Coordination](https://mlsanity.com/paper/2307.06620/) (2023)
- [Distributed Automatic Generation Control subject to Ramp-Rate-Limits: Anytime Feasibility and Uniform Network-Connectivity](https://mlsanity.com/paper/2509.06588/) (2025)

## Abstract (arXiv)

In the rapidly evolving research on artificial intelligence (AI) the demand for fast, computationally efficient, and scalable solutions has increased in recent years. The problem of optimizing the computing resources for distributed machine learning (ML) and optimization is considered in this paper. Given a set of data distributed over a network of computing-nodes/servers, the idea is to optimally assign the CPU (central processing unit) usage while simultaneously training each computing node locally via its own share of data. This formulates the problem as a co-optimization setup to (i) optimize the data processing and (ii) optimally allocate the computing resources. The information-sharing network among the nodes might be time-varying, but with balanced weights to ensure consensus-type convergence of the algorithm. The algorithm is all-time feasible, which implies that the computing resource-demand balance constraint holds at all iterations of the proposed solution. Moreover, the solution allows addressing possible log-scale quantization over the information-sharing channels to exchange log-quantized data. For some example applications, distributed support-vector-machine (SVM) and regression are considered as the ML training models. Results from perturbation theory, along with Lyapunov stability and eigen-spectrum analysis, are used to prove the convergence towards the optimal case. As compared to existing CPU scheduling solutions, the proposed algorithm improves the cost optimality gap by more than 50%.