# A smart resource management mechanism with trust access control for cloud computing environment

arXiv: https://arxiv.org/abs/2212.05319 · HTML: https://mlsanity.com/paper/2212.05319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 107 references that are in the map.
- First posted on arXiv 10 Dec 2022.

## Builds on (most cited first, 19 of 19)

- [Credit Card Fraud Detection using Machine Learning: A Study](https://mlsanity.com/paper/2108.10005/) (2021)
- [A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads](https://mlsanity.com/paper/2211.14619/) (2022)
- [A Holistic View on Data Protection for Sharing, Communicating, and Computing Environments: Taxonomy and Future Directions](https://mlsanity.com/paper/2202.11965/) (2022)
- [HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model](https://mlsanity.com/paper/2203.08143/) (2022)
- [workload forecasting and resource management models based on machine learning for cloud computing environments](https://mlsanity.com/paper/2106.15112/) (2021)
- [TIDF-DLPM: Term and Inverse Document Frequency based Data Leakage Prevention Model](https://mlsanity.com/paper/2203.05367/) (2022)
- [A High Availability Management Model based on VM Significance Ranking and Resource Estimation for Cloud Applications](https://mlsanity.com/paper/2211.16117/) (2022)
- [Security and Privacy-Preservation of IoT Data in Cloud-Fog Computing Environment](https://mlsanity.com/paper/2212.00321/) (2022)
- [A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center](https://mlsanity.com/paper/2212.01896/) (2022)
- [A study on Machine Learning Approaches for Player Performance and Match Results Prediction](https://mlsanity.com/paper/2108.10125/) (2021)
- [A Survey and Comparative Study on Multi-Cloud Architectures: Emerging Issues And Challenges For Cloud Federation](https://mlsanity.com/paper/2108.12831/) (2021)
- [A Discussion and Comparative Study on Security and Privacy of Smart Meter Data](https://mlsanity.com/paper/2111.09227/) (2021)
- [MLRM: A Multiple Linear Regression based Model for Average Temperature Prediction of A Day](https://mlsanity.com/paper/2203.05835/) (2022)
- [A Comprehensive Vision on Cloud Computing Environment: Emerging Challenges and Future Research Directions](https://mlsanity.com/paper/2207.07955/) (2022)
- [Efficient Resource Management in Cloud Environment](https://mlsanity.com/paper/2207.12085/) (2022)
- [A Fault Tolerant Elastic Resource Management Framework Towards High Availability of Cloud Services](https://mlsanity.com/paper/2212.03547/) (2022)
- [Testable Designs of Toffoli Fredkin Reversible Circuits](https://mlsanity.com/paper/2108.07448/) (2021)
- [PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classification](https://mlsanity.com/paper/2203.11287/) (2022)
- [Dynamic Resource Allocation Method for Load Balance Scheduling over Cloud Data Center Networks](https://mlsanity.com/paper/2211.02352/) (2022)

## Abstract (arXiv)

The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. It provides infinite computing capabilities through its massive cloud datacenters, in contrast to early distributed computing models, and has been incredibly popular in recent years because to its continually growing infrastructure, user base, and hosted data volume. This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency, assuring the safe execution of users' applications, and protecting against data breaches brought on by unauthorised virtual machine access real-time. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication. Additionally, a workload analyzer unit works simultaneously to estimate resource consumption data to help the resource management unit be more effective during virtual machine allocation. The suggested model functions differently to effectively serve the same objective, including data encryption and decryption prior to transfer, usage of trust access mechanism to prevent unauthorised access to virtual machines, which creates extra computational cost overhead.