Virtual Machine Consolidation Research Articles

Joint VM and container consolidation with auto-encoder based contribution extraction of decision criteria in Edge-Cloud environment

In the recent years, emergence huge Edge-Cloud environments faces great challenges like the ever-increasing energy demand, the extensive Internet of Things (IoT) devices adaptation, and the goals of efficiency and reliability. Containers has become increasingly popular to encapsulate various services and container migration among Edge-Cloud nodes may enable new use cases in various IoT domains. In this study, an efficient joint VM and container consolidation solution is proposed for Edge-Cloud environment. The proposed method uses the Auto-Encoder (AE) and TOPSIS modules for two stages of consolidation subproblems, namely, Joint VM and Container Multi-criteria Migration Decision (AE-TOPSIS-JVCMMD) and Edge-Cloud Power SLA Aware (AE-TOPSIS-ECPSA) for VM placement. The module extracts the contribution of different criteria and computes the scores of all the alternatives. Combining the non-linear contribution learning ability of the AE algorithm and the intelligent ranking of the TOPSIS algorithm, the proposed method successfully avoids the bias of conventional multi-criteria approaches toward alternatives that have good evaluations in two or more dependent criteria. The simulations conducted using the Cloudsim simulator confirm the effectiveness of the proposed policies, demonstrating to 41.5%, 30.13%, 12.9%, 10.3%, 58.2% and 56.1% reductions in energy consumption, SLA violation, response time, running cost, number of VM migrations, and number of container migrations, respectively in comparison with state of the arts.

Enhanced Ant Colony Based VM Selection and Consolidation for Energy Conservation

Cloud Computing (CC) involves extensive data centers with numerous computing nodes that consume significant electrical energy. Researchers have identified high service-level agreement (SLA) violations and excessive energy consumption (EC) as major challenges in CC. Traditional approaches often focus on reducing EC but tend to overlook SLA violations, particularly when selecting Virtual Machines (VMs) from overloaded hosts. To address these issues, this paper introduces the Enhanced Ant Colony Optimization (EACO) algorithm, aims to reduce high EC and SLA violations by utilizing a unique approach where the best-performing ant explores movement patterns and identifies distances between movements. The algorithm comprises three key steps: tracking pheromone trails, updating pheromones and selecting the cities (VMs). The effectiveness of EACO was validated through simulations using CloudSim. Compared to existing techniques, EACO demonstrated a significant reduction in EC, achieving approximately 41-44% lower energy consumption than the traditional Ant Colony Optimization (ACO) algorithm when applied to Planet Lab data. This suggests that EACO offers a more efficient and stable solution for managing EC and SLA violations in cloud environments.

Efficient energy consumption in hybrid cloud environment using adaptive backtracking virtual machine consolidation

Virtualization is the process of sharing physical machine resources into multiple Virtual Machines (VMs) for the effective utilization of resources. The major issues are in handling physical resources such as high demand of resources, infrastructure issues and operational issues. Also violating Service Level Agreement (SLA) is another problem to lead the violations. In this case, VM consolidation method is used to migrate the VMs and optimize the efficient way to use the resources. So we get minimized energy consumptions. Existing methods has increased energy consumption and overhead issues. In this paper we propose adaptive backtracking methods to provide VM consolidation based on less energy consumptions. We use Adaptive Hill Climbing and Pursuit algorithms to consolidate the VMs and use the VMs with efficient energy consumption. We implement this simulations using Matlab in Hybrid Cloud Environment. The performance factors are obtained with less energy consumption and compare the results with existing methods. Overall our proposed system is achieving 95% of accuracy index with lower energy consumption such as 28%, 30% and 32% with multiple VMs consolidations.

EBWO‐GE: An innovative approach to dynamic VM consolidation for cloud data centers

SummaryCloud data centers (CDCs) have revolutionized global computing by offering extensive storage and processing capabilities. Nevertheless, the environmental impact of these processes, including their substantial energy consumption and carbon emissions, calls for implementing more efficient techniques. Efficient virtual machine (VM) consolidation is crucial in optimizing resource utilization and reducing energy consumption. Current methods for enhancing energy efficiency often lead to issues such as service level agreements (SLAs) violations and quality of services (QoS) degradation. This study presents a novel approach to host selection using a grey‐extreme (GE) machine learning model, which accurately predicts over and underutilized hosts. In addition, a VM placement technique called enhanced black widow optimization (EBWO) utilizes black widow optimization heuristic techniques and a differential evolutionary approach to optimize VM placement. The proposed dynamic VM consolidation technique optimizes energy utilization while meeting strict SLA requirements and enhancing QoS metrics in CDCs. Extensive analyses were conducted using the Cloudsim toolkit to validate the approach's effectiveness. These analyses encompassed conditions such as random workloads in heterogeneous environments. The simulation results showed that GE‐EBWO outperforms other techniques and improves energy efficiency by 12%–15%. In addition, it significantly decreases VM migrations by 11%–14% compared to other advanced methods. The study validates the practicality of the proposed technique in moving towards environmentally friendly CDCs.

Energy- and Resource-Efficient VM Consolidation Based on Sailfish Optimization Model for Fog and Cloud Infrastructure

Energy- and Resource-Efficient VM Consolidation Based on Sailfish Optimization Model for Fog and Cloud Infrastructure

A PPR-based energy-efficient VM consolidation in cloud computing

A PPR-based energy-efficient VM consolidation in cloud computing

Perspective of virtual machine consolidation in cloud computing: a systematic survey

Perspective of virtual machine consolidation in cloud computing: a systematic survey

Utilizing multi-population ant colony system and exponential grey prediction model for multi-objective virtual machine consolidation in Cloud Data Centers

Utilizing multi-population ant colony system and exponential grey prediction model for multi-objective virtual machine consolidation in Cloud Data Centers

Efficient Resource Management in Cloud Environments: A Modified Feeding Birds Algorithm for VM Consolidation

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers.

EMaC: Dynamic VM Consolidation Framework for Energy-Efficiency and Multi-metric SLA Compliance in Cloud Data Centers

EMaC: Dynamic VM Consolidation Framework for Energy-Efficiency and Multi-metric SLA Compliance in Cloud Data Centers

E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

A novel virtual machine consolidation algorithm with server power mode management for energy-efficient cloud data centers

A novel virtual machine consolidation algorithm with server power mode management for energy-efficient cloud data centers

Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation

Cloud computing is a thriving and ever-expanding sector in the industry world. This growth has sparked increased interest from organizations seeking to harness its potential. However, the sheer volume of services and offerings in this field has resulted in a noticeable surge in related data. With the rapid evolution and growing demand, cloud computing resource management faces a fresh set of challenges. Resource limitations, such as high maintenance costs, elevated Energy Consumption (EC), and adherence to Service Level Agreements (SLA), are critical concerns for both the cloud computing industry and its user organizations. In this context, taking a proactive approach to resource management and Virtual Machine Consolidation (VMC) has become imperative. The logical management of resources and the consolidation of Virtual Machines (VMs) in a manner that aligns with the requirements and demands of service providers and users have garnered widespread attention. The goal of this proposed paper is to focus on addressing the VMC problem within a unified framework, divided into two main phases. The first phase deals with host workload detection and prediction, while the subsequent phase tackles the selection and allocation of appropriate VMs. In our proposed method, for the first time, we use a Granular Computing (GRC) model, which is an efficient, scalable, and human-centric computational approach. This model exhibits behaviors similar to intelligent human decision-making, as it can simultaneously consider all factors and criteria involved in the problems. We evaluated our proposed method through simulations using CloudSim on various types of workloads. Experimental results demonstrate that our proposed algorithm outperforms other algorithms in all measurement metrics.

An efficient dynamic migration and consolidation method of VMs based on improved K-nearest neighbor algorithm and attribute weighting

Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover’s distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration.

Deep Learning Modified Reinforcement Learning with Virtual Machine Consolidation for Energy-Efficient Resource Allocation in Cloud Computing

Cloud computing has attracted significant attention because of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Cloud data centers demand massive amounts of electrical power as modern applications and organizations grow. To prevent resource waste and promote energy efficiency, virtual machines (VMs) must be dispersed over numerous physical machines (PMs) in a data center in the cloud. The actual allocation of VMs to PMs can involve more complex decision-making processes, such as considering the resource utilization, load balancing, performance requirements, and constraints of the system. Advanced techniques, like intelligent placement algorithms or dynamic resource allocation, may be employed to optimize resource utilization and achieve efficient VM distribution across multiple PMs. Cloud service suppliers aim to lower operational expenses by reducing energy consumption while offering clients competitive services. Minimizing large-scale data center power usage while maintaining the quality of service (QoS), especially for social media-based cloud computing systems, is crucial. Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented reinforcement learning (RL)-based energy efficient and QoS-aware virtual machine consolidation (VMC) approach to meet the difficulties. The proposed deep learning modified reinforcement learning-virtual machine consolidation (DLMRL-VMC) model can motivate both cloud providers and customers to distribute cloud infrastructure resources to achieve high CPU utilization and good energy efficiency as measured by power usage effectiveness (PUE) and data center infrastructure efficiency (DCiE). The suggested model, DLMRL-VMC, offers a VM placement approach based on resource usage and dynamic energy consumption to determine the best-matched host and VM selection strategy, Average Utilization Migration Time (AUMT). Based on AUMT, deep learning modified reinforcement learning (DLMRL) will choose a VM with a low average CPU utilization and a short migration time. The DLMRL-VMC Energy-efficient, Resource Allocation strategy is evaluated on the trace of the CloudSim VM to attain good PUE and CPU utilization.

Energy and Performance Centric Resource Allocation Framework in Virtual Machine Consolidation Using Reinforcement Learning Approach

Abstract:: 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.

On the scalability of the speedup considering the overhead of consolidating virtual machines in servers for data centers

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

A Review Study on Energy Consumption in Cloud Computing

Cloud computing has become a fundamental technology for a wide range of computing services, yet its increasing energy demands present substantial environmental and economic challenges. With the rapid growth of Cloud services and applications, increasing number of researches have been focused on energy saving. The need to reduce energy costs is a constant challenge of cloud providers and data centers. This paper offers an extensive review of the issues surrounding energy consumption in cloud computing, with a focus on algorithms associated with the situational awareness, consolidation, allocation, placement/migration, and scheduling of virtual machines and containers. We conduct a critical analysis of studies from 2018 to 2023, comparing various methodologies aimed at achieving energy efficiency without sacrificing performance. This review delineates current trends, identifies gaps in existing research, and proposes directions for future investigations. Our study emphasizes the necessity of cultivating sustainable practices in cloud computing and provides valuable insights into the practical implementation of energy-efficient solutions in cloud environments.

A cut-and-solve algorithm for virtual machine consolidation problem

The virtual machine consolidation problem attempts to determine which servers to be activated, how to allocate virtual machines to the activated servers, and how to migrate virtual machines among servers such that the summation of activated, allocation, and migration costs is minimized subject to the resource constraints of the servers and other practical constraints. In this paper, we first propose a new mixed integer linear programming formulation for the virtual machine consolidation problem. We show that compared with existing formulations, the proposed formulation is much more compact in terms of smaller numbers of variables or constraints, which makes it suitable for solving large-scale problems. We then develop a cut-and-solve algorithm, a tree search algorithm to efficiently solve the virtual machine consolidation problem to optimality. The proposed cut-and-solve algorithm is based on a novel relaxation of the virtual machine consolidation problem that provides a stronger lower bound than the natural continuous relaxation of the virtual machine consolidation problem, making a smaller search tree. By extensive computational experiments, we show that (i) the proposed formulation significantly outperforms existing formulations in terms of solution efficiency; (ii) compared with standard mixed integer linear programming solvers, the proposed cut-and-solve algorithm is much more efficient; and (iii) compared with an existing heuristic algorithm, the proposed cut-and-solve algorithm is better able to balance workload distribution across servers and achieve higher resource utilization.

Dynamic Optimization of Energy Efficiency and Service Level Agreement violation in Cloud Data Centers via Virtual Machine Consolidation

With the escalating proliferation of cloud computing services, cloud data center providers are actively in pursuit of solutions not only to curtail energy consumption but also to efficiently cater to the unique demands of their users, thereby enhancing user satisfaction. The process of consolidating virtual machines emerges as a viable strategy for striking a balance between energy consumption and Service Level Agreement violation (SLAv), potentially surmounting this predicament faced by cloud service providers. This study aims to minimize energy consumption and SLAv to their utmost feasible extent by utilizing the First Median Threshold (FMT) technique for physical host selection and virtual machine allocation, in conjunction with the Maximum Ratio of CPU utilization to memory Utilization (MRCU) method for virtual machine selection within the framework of virtual machine consolidation. This dual-pronged approach not only translates into cost savings for cloud providers but also ensures the delivery of optimal services to users. Furthermore, a novel method has been introduced for selecting virtual machines for migration and appropriately choosing physical hosts to achieve equilibrium between two critical parameters: energy consumption and SLAv. The proposed methodology is implemented and evaluated using the CloudSim simulator, utilizing real-world PlanetLab data. The comprehensive analysis of the simulation results reveals that the proposed approach can significantly reduce energy consumption while effectively mitigating SLAv for users.