2026· EPJ Web of Conferences· Vol 380, pp. 02017· 0 citations· 6 references
TL;DR
This article proposes a system for dynamic optimization of virtual machines in a cloud to satisfy the multiple and varied requests of users and ensures the optimal use of data centre resources.
Abstract
Cloud computing appears to be an ecosystem with flexible and scalable IT resources. The finding is that cloud services are increasing considerably and resource management is a real problem. This is the case with the static allocation of virtual machines (VMs) that fail to effectively manage multiple workloads in real time. This leads to inefficiencies and high costs. This article proposes a system for dynamic optimization of virtual machines in a cloud to satisfy the multiple and varied requests of users. The main objective is to design a system that dynamically adjusts the number and configurations of VMs according to cloudlet requests, while optimizing performance and costs. This solution allows to learn, scale and remove virtual machines taking into account the variation in demand. It therefore ensures the optimal use of data centre resources.
Cloud computing has seen rapid growth in recent years, leading to a surge in demand for data center services. To meet this demand, data centers deploy a large number of servers, resulting in substantial energy consumption. Virtual Machine Consolidation (VMC) is an effective strategy to reduce energy usage by shutting down underutilized servers while ensuring that Service Level Agreements (SLAs) are maintained. The VMC process comprises four key steps: detecting overloaded hosts, identifying underloaded hosts, selecting virtual machines (VMs), and determining their placement. This research presents the Energy-Efficient Virtual Machine Placement (EEVMP) approach, which aims to optimize resource utilization by selecting suitable destination hosts for migrating VMs based on utilization and resource skewness. The proposed method is evaluated using the CloudSim simulator. Experimental results show that EEVMP consistently outperforms existing placement strategies such as PABFD, IQRMC, PEBFD, ESVMP, and HVMAP in terms of energy efficiency and overall performance.
Dipak Dabhi, A. Kharwar, D. Vadhwani et al.· ITEGAM- Journal of Engineeri...· 0 citations
This analysis identifies critical VM scheduling trade-offs, provides optimization guidelines, and validates the efficacy of hybrid adaptive methods via a new proposed heuristic-machine learning model for dynamic cloud environments.
Chaimae Bahij, Mohamed El Ghmary, Hassan Echoukairi· EPJ Web of Conferences· 0 citations
The findings suggest that HORAM is far better at using resources; fewer tasks are completed, and the total power consumed is lower than with traditional scheduling algorithms, suggesting the suggested architecture is a viable solution to sustainable cloud infrastructure management.
S. Balakrishnan, K. Aravind, T. Veeramani et al.· SN Computer Science· 0 citations
The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms.
Farah Al-Farsi· International Journal of App...· 0 citations
Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service.
Eram Fatma, Nidhi Mishra, Mohammed Abdul Bari· Journal of Intelligent Decis...· 0 citations
The design and development of DynamiCloud is presented, a scalable and computationally efficient multi-objective dynamic resource allocation model for cloud computing that can simultaneously optimize multiple conflicting objectives such as throughput, Service Level Agreement compliance, and power efficiency.
Onwuegbuchulem Gift., E. O. Bennett, M. D. et al.· Journal of Artificial Intell...· 0 citations
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