Consolidation of virtual machines to reduce energy consumption of data centers by using ballooning, sharing and swapping mechanisms

Cloud data centers consume massive energy as workloads continue to grow. This study explores an energy-aware resource allocation framework that combines workload prediction and dynamic VM consolidation to reduce power consumption without compromising performance. The model uses historical utilization patterns to forecast demand and allocates resources accordingly. Experimental results on real datasets show noticeable reductions in energy use and SLA violations. The work contributes to sustainable cloud computing by balancing efficiency and reliability.

HAPSO: An ACO-initialized, discretization-aware PSO for energy- and carbon-efficient VM consolidation in green cloud datacenters

Multi-resource aware virtual machine consolidation approach for modern cloud data centers

The surging energy requirements and the impact on the environment of the cloud data center suggests the necessity of establishing intelligent resource management plans to facilitate sustainability. The proposed hybrid framework which incorporates hybrid dynamic virtual machine (VM) consolidation based on the idea of artificial intelligence (AI) with the concept of renewable-aware differences in this paper to decrease energy and carbon emission in cloud computing environment. The consolidation module employs reinforcement learning to dynamically locate and move VMs based on real time workload and server utilization patterns. Concurrently, a renewable-sensitive scheduler predicts availability of solar energy and wind energy and executes tasks around green energy highlights. The framework is tested with the help of CloudSim Plus, actual Google Cluster traces, and synthetic renewable traces. The findings indicated that there was a decrease of energy of 37 percent and carbon emission decrease of 46.7 percent over the traditional models with a high SLA compliance along with minimizing the VM migration overhead. The given solution has a great potential to become an energy-efficient and environmentally-friendly cloud infrastructure that does not worsen performances.

Energy-Aware Virtual Machines Consolidation in Cloud Environments Using Dynamic Thresholding and Ant Colony Optimization

Introduction: Virtual machines are used to reduce cloud platform application performance, management costs, and access irregularities. Virtual machines are frequently vulnerable to delays, overburdening workloads, and other obstacles while consolidating and migrating servers. To significantly disperse loads among virtual machines, dynamic consolidation techniques are implemented to control energy dissipation, monitor overloading, and address underloading problems. Background: The process of consolidation involves more calculations and resources in order to transfer services between virtual machines, provided that Service Level Agreements are observed. Methods: The suggested approach promotes the use of cutting-edge architecture to combine virtual machines, and, therefore, strike a balance between performance and energy requirements. The main design considerations for the suggested Dynamic Weightage algorithm, which includes the clustering approach in relation to reinforcement learning approaches, are overall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines is created, and resources are distributed according to performance and energy requirements. Virtual machine resource requests are converted into a matching relationship factor, which represents the individual hosts while taking PPR into account. The overall workload associated with virtual machine consolidation is also provided by these estimations. It is noted that there is little energy trade-off and that performance is maintained at a nominal level across the cluster. The architecture is put into practice throughout offline platforms, which are dispersed ecosystems that allow for increased system performance and scaling. Results: The CloudSim simulator is used to validate the system using datasets that are obtained from PlanetLab. According to the data, energy saving has produced yields of up to 47% and promising quality of service attributes. Conclusion: The validation of the system is performed using the CloudSim simulator with datasets from PlanetLab. The results indicate significant energy conservation, up to 47%, along with promising quality of service parameters. The proposed architecture is compared with other state-of-the-art algorithms for distributed architectures and heterogeneous environments, showcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation and energy efficiency in the proposed architecture, which has been tested on a Proliant G7- based data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming OpenStack-based techniques in simulation results.

RLSK_US: An Improved Dynamic Virtual Machine Consolidation Model to Optimize Energy and SLA Violations in Cloud Datacenter

Reducing energy consumption and optimizing resource usage are essential goals for researchers and cloud providers managing large cloud data centers. Recent advancements have demonstrated the effectiveness of virtual machine consolidation and live migrations as viable solutions. However, many existing strategies are based on immediate workload fluctuations to detect host overload or underload and trigger migration processes. This approach can lead to frequent and unnecessary VM migrations, resulting in energy inefficiency, performance degradation, and service-level agreement (SLA) breaches. Moreover, traditional time series and machine learning models often struggle to accurately predict the dynamic nature of cloud workloads. This paper presents a consolidation strategy based on predicting resource utilization to identify overloaded hosts using novel hybrid value trend generative adversarial network (VTGAN) models. These models not only predict future workloads but also forecast workload trends (i.e., the upward or downward direction of the workload). Trend classification can simplify the decision-making process in resource management approaches. We perform simulations using real PlanetLab workloads on Cloudsim to assess the effectiveness of the proposed VTGAN approaches, based on value and trend, compared to the baseline algorithms. The experimental findings demonstrate that the VTGAN (Up current and predicted trends) approach significantly reduces SLA violations and the number of VM migrations by 79% and 56%, respectively, compared to THR-MMT-PBFD. Additionally, incorporating VTGAN into the VM placement algorithm to disregard hosts predicted to become overloaded further improves performance. After excluding these predicted overloaded servers from the placement process, SLA violations and the number of VM migrations are reduced by 84% and 76%, respectively, compared to THR-MMT-PBFD.

Efficient VM consolidation: deep reinforcement learning approach based PM workload awareness

With the evolution of cloud computing (CC) technologies, there is a growing insistence for the maximum utilization of cloud resources, therefore increasing the computing power consumption of cloud’s systems. Cloud’s Virtual Machines (VMs) consolidation imparts a practical mechanism to minimize energy consumption of cloud Data Centers (DC). Efficient consolidation and migration of VM in the absence of infringing Service Level Agreement (SLA) can be arrived at by making decisions proactively based on cloud’s future workload prediction. Efficient load balancing, another major issue of CC also depends on accurate forecasting of resource usage. Cloud workload traces reveal both periodic and non-periodic patterns with the unexpected peak of load. As a result, it is very demanding for the prediction models to accurately anticipate future workload. This prompted us to propose a method called, Attention Bidirectional Gated and Weight-adaptive Sparrow Search Optimization (ABiG-WSSO) to accurately forecast future workload with minimal makespan and overhead. The proposed ABiG-WSSO method includes Attention Bidirectional Gated Recurrent Unit (ABiGRU) and Weight-adaptive Sparrow Search Optimization (WSSO). Attention Bidirectional Gated Recurrent Unit (ABiGRU) is initially designed that along with the use of Bidirectional Gated Recurrent Unit (BiGRU) and adaptation of attention mechanism aids in predicting future cloud load requirements accurately. Next, Weight-adaptive Sparrow Search Optimization (WSSO) algorithm is employed in fine-tuning the parameters of the ABiGRU model for accurate and optimal load balancing performance. The WSSO algorithm is applied to optimize ABiGRU model hyperparameters (i.e. learning rate), to enhance its prediction accuracy. Comprehensive simulations are carried out using the gwa-bitbrains dataset to verify the efficiency of the proposed ABiG-WSSO method in boosting the distribution of resources and cloud load balancing. The proposed method achieves comparatively better results in terms of better makespan time, energy consumption, associated overhead and throughput.

ABSTRACTThe increase in popularity and demand for cloud services has caused a huge growth of cloud data centers, and this has caused the challenge of energy management in data centers. Virtual Machine (VM) consolidation is a critical process aimed at optimizing resource utilization and minimizing energy usage. VM consolidation with the turnoff of underloaded hosts and reducing the load of overloaded hosts establishes a balance between energy consumption and SLA violations. In fact, the consolidation process includes three sub‐problems: determining overloaded and underloaded hosts, VM selection in overloaded hosts, and finding a new destination for VMs that will be migrated (VM placement). This paper introduces an entropy‐based approach to VM selection and placement to improve efficiency in cloud data centers. Entropy is a quantifiable characteristic often linked to disorder, randomness, or unpredictability. By leveraging entropy as a measure of workload distribution and uncertainty, the proposed method effectively predicts future resource demands, enabling informed decisions that enhance energy efficiency and reduce SLA violations. A key advantage of this approach is the significant reduction in the number of VM migrations, which decreases overhead and minimizes potential service disruptions. Experimental results demonstrate that our entropy‐based method outperforms the VM consolidation process in terms of energy consumption, SLA compliance, and system stability. The findings suggest that this approach offers a more sustainable and cost‐effective solution for managing cloud resources, contributing to the development of efficient and reliable cloud computing environments.

Efficient virtual machine (VM) migration and consolidation are critical for optimizing resource utilization, reducing energy consumption, and ensuring service continuity in cloud-based environments. This study introduces the Unpredictability-Weighted Time Backward Expectation Algorithm (UW-TBEA), a novel approach designed to enhance VM migration and consolidation processes. UW-TBEA dynamically adjusts migration decisions by incorporating a backward expectation framework that is weighted by the unpredictability of resource demands over time. By assessing the unpredictability of workloads, UW-TBEA prioritizes VM movements to maintain balanced resource allocation while minimizing service-level agreement (SLA) violations. Experimental results demonstrate that UW-TBEA outperforms traditional consolidation techniques by reducing migration frequency by 18%, lowering energy consumption by 22%, and decreasing SLA violations by 15%. The proposed algorithm offers a robust solution for cloud service providers to achieve cost-effective, scalable, and energy-efficient operations in dynamic and unpredictable environments.

Cloud Provider (CP) offers resources to the various categories of clients according to the consumer's required demand for quality of service (QoS). When a physical machine (PM) is overloaded, the performance of its virtual machines (VMs) may degrade. Idle PMs can be shut down to conserve energy. This paper introduces a new approach for resource provisioning through VM consolidation and migration, aiming to meet user demands, minimize Service Level Agreement (SLA) violations, and reduce performance degradation during resource shortages. Initially, the workload of PMs for future time interval is predicted from the workloads of several previous time intervals of PMs using deep learning. If resource utilization across PMs is uneven, the resource provisioning method is regularly activated during these intervals.

Abstract Cloud computing faces challenges in energy consumption and quality of service (QoS). Virtual machine (VM) consolidation, involving relocation between hosts, helps reduce power usage and enhance QoS. OpenStack Neat, a leading VM consolidation framework, uses the modified best‐fit decreasing (MBFD) strategy but faces energy consumption and QoS issues. To address these, we present the secure energy efficient VM consolidation (SEEVMC) method, introducing a unique host selection criterion based on incurred loss during VM placement. We evaluated SEEVMC with real‐time workload data from PlanetLab and Materna over ten days using CloudSim. For PlanetLab, SEEVMC reduced energy consumption by 78.33%, 57.74%, 19.57%, and 6.30% and reduced system‐level agreement (SLA) violations by 92.49%, 92.78%, 45.16%, and 15.67%, compared with MBFD, power‐aware best fit decreasing, medium fit power efficient decreasing, and power‐efficient bit decreasing. For Materna, SEEVMC reduced energy consumption by 14.12%, 59.5%, 3.92%, and 3.80% and fewer SLA violations by 74.85%, 86.95%, 11.40%, and 46.60%. SEEVMC also reduced VM migrations and SLA time per active host, improving cloud computing efficiency.

An Energy-Aware Approach to Virtual Machine Consolidation Using Classification and the Dragonfly Algorithm in Cloud Data Centers

This study addresses the challenge of virtual machine (VM) placement in cloud computing to improve resource utilization and energy efficiency. We propose a mixed integer linear programming (MILP) model incorporating -robustness theory to handle uncertainties in VM usage, optimizing both performance and energy consumption. A heuristic algorithm is developed for large-scale VM allocation. Experiments with Huawei Cloud data demonstrate significant improvements in resource utilization and energy efficiency.

Introduction: The substantial energy usage in the cloud computing system is a significant drawback both for the cloud providers along with the cloud service users. In order to decrease the amount of energy used, it is necessary to implement virtualization techniques. Objectives: this paper employs a GA to optimize the migration of VMs and reduce energy usage in a cloud environment. Methods: The VM consolidation method effectively handles cloud resources, meeting the needs of both cloud consumers and suppliers. Moreover, it helps in enhancing the efficiency of servers while simultaneously decreasing the excessive energy usage in data canters. Nevertheless, the unnecessary activities of the VM consolidation technique result in inadequate VM selection and improper VM assignment, causing low performance, QoS, and violations of SLAs. Data center management struggles with energy consumption. VM migration and placement works well for this. Data centers require energy-saving solutions without affecting other parameters. Results: The performance of the proposed method has been evaluated utilizing factors like CPU usage, memory usage, network speed, power usage, and SLA breaches. Conclusions: The comparative analysis of the proposed approach with existing methods highlights its effectiveness and trustworthiness.

Fog computing brings cloud-like services closer to data sources, improving responsiveness but also introducing challenges like high energy consumption and inefficient resource use. To tackle this, VM consolidation using live migration is employed to enhance energy efficiency and resource management. This paper introduces a hybrid Genetic Algorithm model that combines various selection strategies—Tournament, Rank, SUS, Truncation, and Roulette-Wheel—to optimize CPU and memory usage during VM consolidation. By using heuristics for population generation, fitness evaluation, and load balancing, the model minimizes active physical machines and adapts to workload changes, improving efficiency in fog data centers.

Energy consumption has become a common problem since days. Addressing the energy related problem is a challenging task. There are various strategies present to minimize this problem. One among them is using cloud computing infrastructure and VM setup. Virtual Machine consolidation is a viable solution to mitigate energy related issues of data centres. In recent times, we have seen various learning approaches which are used in managing the cloud data resources well. Among the approaches, Virtual Machine consolidation technique gives the viable solution for energy related issues by mitigating them. We have also delved with reinforcement learning algorithm to tackle the virtual machines. In this implementation we make use of different RL algorithms such as SARSA, Q-learning etc. and finds out the best suited algorithm. Furtherly, we will execute the model on using the algorithm chosen to build the model. The inputs we take are VM numbers, power utilization, scalability of VMs, CPU utilization time etc. and finds out what percentage of these values we are getting as an output which highlights the effectiveness of our approach, improvement in energy efficiency and service reduction etc.