# workload forecasting and resource management models based on machine learning for cloud computing environments

arXiv: https://arxiv.org/abs/2106.15112 · HTML: https://mlsanity.com/paper/2106.15112/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2021.

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

- [A Privacy-Preserving Outsourced Data Model in Cloud Environment](https://mlsanity.com/paper/2211.13542/) (2022)
- [Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services](https://mlsanity.com/paper/2507.12908/) (2025)
- [A smart resource management mechanism with trust access control for cloud computing environment](https://mlsanity.com/paper/2212.05319/) (2022)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)

## Abstract (arXiv)

The workload prediction and resource allocation significantly play an inevitable role in production of an efficient cloud environment. The proactive estimation of future workload followed by decision of resource allocation have become a prior solution to handle other in-built challenges like the under/over-loading of physical machines, resource wastage, Quality-of-Services (QoS) violations, load balancing,VM migration and many more. In this context, the paper presents a comprehensive survey of workload forecasting and predictive resource management models in cloud environment. A conceptual framework for workload forecasting and resource management, categorization of existing machine learning based resources allocation techniques, and major challenges of inefficient distribution of physical resource distribution are discussed pertaining to cloud computing. Thereafter, a thorough survey of existing state-of-the-art contributions empowering machine learning based approaches in the field of cloud workload prediction and resource management are rendered. Finally, the paper explores and concludes various emerging challenges and future research directions concerning elastic resource management in cloud environment.