2026· EPJ Web of Conferences· 0 citations· 4 references
TL;DR
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.
Abstract
This paper explores sophisticated Virtual Machine (VM) scheduling approaches in cloud computing and their significance to enhance resource distribution, improve system efficiency, and cost reduction. It provides a recent overviews of key scheduling algorithms, including heuristic, metaheuristic, advanced machine learning-based and hybrid approaches, while assessing their respective strengths, weaknesses and practical applications. The discussion encompasses their applications in managing workloads, optimizing costs, enhancing energy efficiency, improving Quality of Service (QoS) and with a particular focus on scalability and real-time scheduling in cloud settings. Furthermore, the paper analyzes scheduling strategies adopted by major cloud providers through real-world case studies. Ultimately, our 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.
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 d...
Dipak Dabhi, A. Kharwar, D. Vadhwani et al.· ITEGAM- Journal of Engineeri...· 0 citations
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.
Aziz Saibou, Onyonkiton Theophile Aballo, Arsene Narcisse Dagba et al.· EPJ Web of Conferences· 0 citations
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addres...
M. Yacoub, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Electronics· 0 citations
The current invention outlines a Java-driven framework for efficient resource management in cloud data centres using the CloudSim simulation environment. The framework presents a predictive auto-scaling mechanism that examines historical traffic patterns to forecast future workload requirements, allowing for predictive...
S. Divya, P. Venkadesh, G. Vasunthraa et al.· International Conference on...· 0 citations
A hybrid nature-inspired algorithm called fruit fly optimization–ant colony optimization (FOA-ACO), which combines the exploitative ant colony optimization (ACO) and the exploratory fruit fly optimization algorithm (FOA) is suggested, which enhances overall cloud performance.
Narayana Rao Appini, K. Premnadh, Karnam Sreenu et al.· Int. J. Online Biomed. Eng.· 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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