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 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 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 addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.
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 Virtual Machine (VM) al- location rather than traditional fixed threshold-based techniques. Prior to VM migration, the system assesses a Service Level Agreement (SLA) risk factor to avoid performance degradation and potential SLA violations through intelligent power management. A specific Green Scheduler Algorithm dynamically consolidates Virtual Machines by allocating workloads to optimally loaded physical machines based on fore- casted workload conditions. Machines with low utilization are automatically migrated across different power-saving states, such as idle, sleep, and deep sleep modes. This comprehensive framework strikes a balance between energy savings and the preservation of service reliability and Quality of Service (QoS). Simulation results demonstrate the effectiveness of energy savings, improved resource utilization, and SLA compliance, making it suitable for scalable and ecofriendly cloud resource management.
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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