# Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency

arXiv: https://arxiv.org/abs/2509.17695 · HTML: https://mlsanity.com/paper/2509.17695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 85 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing](https://mlsanity.com/paper/1712.02719/) (2017)
- [Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions](https://mlsanity.com/paper/2105.04086/) (2021)
- [A Dependable Hybrid Machine Learning Model for Network Intrusion Detection](https://mlsanity.com/paper/2212.04546/) (2022)
- [Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization](https://mlsanity.com/paper/1611.02120/) (2016)
- [Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach](https://mlsanity.com/paper/2401.00534/) (2023)
- [Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators](https://mlsanity.com/paper/1912.12167/) (2019)
- [The Case for Task Sampling based Learning for Cluster Job Scheduling](https://mlsanity.com/paper/2108.10464/) (2021)
- [Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization](https://mlsanity.com/paper/2403.07905/) (2024)
- [DLB: Deep Learning Based Load Balancing](https://mlsanity.com/paper/1910.08494/) (2019)
- [Artificial intelligence based load balancing in SDN: A comprehensive survey](https://mlsanity.com/paper/2308.02149/) (2023)

## Built on by (most cited first, 1 of 1)

- [Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing](https://mlsanity.com/paper/2601.09282/) (2026)

## Abstract (arXiv)

This research investigates how Machine Learning (ML) algorithms can assist in workload allocation strategies by detecting tasks with node affinity operators (referred to as constraint operators), which constrain their execution to a limited number of nodes. Using real-world Google Cluster Data (GCD) workload traces and the AGOCS framework, the study extracts node attributes and task constraints, then analyses them to identify suitable node-task pairings. It focuses on tasks that can be executed on either a single node or fewer than a thousand out of 12.5k nodes in the analysed GCD cluster. Task constraint operators are compacted, pre-processed with one-hot encoding, and used as features in a training dataset. Various ML classifiers, including Artificial Neural Networks, K-Nearest Neighbours, Decision Trees, Naive Bayes, Ridge Regression, Adaptive Boosting, and Bagging, are fine-tuned and assessed for accuracy and F1-scores. The final ensemble voting classifier model achieved 98% accuracy and a 1.5-1.8% misclassification rate for tasks with a single suitable node.