Virtual Machine Consolidation Research Articles

Energy Efficient Optimization with Threshold Based Workflow Scheduling and Virtual Machine Consolidation in Cloud Environment

Energy Efficient Optimization with Threshold Based Workflow Scheduling and Virtual Machine Consolidation in Cloud Environment

A Multi-Objective Adaptive Upper Threshold Approach for Overloaded Host Detection in Cloud Computing

Cloud data centers (CDC) have become an increasingly critical issue because of their large-scale deployment, which has resulted in increased energy consumption (EC) and SLA. The SLA and EC can be greatly reduced by using an efficient virtual machine consolidation (VMC) approach. This study presents a multi-objective adaptive upper threshold (UTh) technique for identifying overloaded hosts. The dynamic virtual machine consolidation (DVMC) is then obtained by combining a modified overloaded host detection technique with a different VM selection method (i.e., minimum migration time (Mmt) and minimum utilization (Mu)). The simulation results indicate that the modified Interquartile range (Iqr) overloaded host detection algorithm outperforms the existing overloaded host detection algorithms (i.e., InterQuartile range (Iqr), local regression (Lr), and dynamic voltage frequency scale (DVFS) algorithms) in terms of EC, SLA, and the number of virtual machine (VM) migrations.

Improving Energy Efficiency in Cloud Computing Data centres Using Intelligent Mobile Agents

Cloud computing has become a profound name and also a solid bedrock for new emerging technology. Cloud technology fully supports the dynamic provisioning of computing resources as a utility service on a pay-as-you-go approach. Its numerous benefits (such as rapid elasticity, flexibility, network resource pooling) empower small, medium, and large enterprises to use other technologies through the Internet's flexibility. Despite the benefits cloud technology offers, the challenge of the high power consumption rate it incurs as it leverages its promised attributes remains a major concern. Academic research has shown that a typical 500 square meter data centre consumes about 27,048 kilowatts per hour of power per day regardless of whether it is active or not. Over the years, the most dominant energy efficient techniques for managing data centre has been Dynamic voltage frequency scaling and virtual machine consolidation, which has had a significant setback due to its inability to manage systems on overload state. Therefore, a novel paradigm based on an intelligent mobile agent approach has been proposed. This proposed approach is highly intelligent can easily detect underutilised and overloaded components of the data centre due to its unique feature. Agent technique has successfully shown it can prevent and manage overloading issues due to change in workloads and achieve a more efficient load balancing with a low power consumption rate. The mobile agent was embedded into servers and switches to regulate their activities and then shut down underutilised components. Mobile agent (Java agent) is the first of its kind used in a cloud environment. This research proposal saves a significant amount of energy and improves the entire system performance. In this thesis, the intelligent-based Agent approach is used to address energy efficiency and costaware related problems. Agent approach helps facilitate resource management, allocation of cloud data centre components with a significant reduction in energy usage rate with a more efficient system performance while maintaining a highly reliable system as promised by service providers.

Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform

In the era of information explosion, the energy consumption of cloud data centers is significant. It’s critical to reduce the energy consumption of large-scale data centers while guaranteeing quality of service (QoS), especially the energy consumption of video cloud computing platforms. The application of virtual machine (VM) consolidation has been regarded as a promising approach to improve resource utilization and save energy of the data centers. In this paper, an energy efficient and QoS-aware VM consolidation method is proposed to address the issues. A combined prediction model based on grey model and ARIMA is applied to host status detection, and we provide a new scheme that VM placement policy based on resource utilization and varying energy consumption to search most suitable host and VM selection policy called AUMT selecting VM with low average CPU utilization and migration time. Extensive experimental results based on the cloudsim simulator demonstrate that proposed approach enables to achieve the objectives reducing energy consumption, number of migrations, SLAV and ESV by an average of 56.07%, 79.21%, 91.01% and 84.34% compared with the benchmark methods and the AUMT can reduce energy consumption, the number of migrations and ESV by an average of 15.46%, 28.11% and 3.96% compared with the state-of-the-art method.

Dynamic Dual-Threshold Virtual Machine Merging Method Based on Three-Way Decision

Cloud computing, an emerging computing paradigm, has been widely considered due to its high scalability and availability. An essential stage of cloud computing is the cloud virtual machine migration technology. Nevertheless, the current trigger timing of virtual machine migration in cloud data centers is inaccurate, resulting in insufficient virtual machine consolidation. Furthermore, the high and low workload fluctuations are also a potential symmetrical problem worthy of attention. This paper proposes a virtual machine energy-saving merging method based on a three-way decision (ESMM-3WD). Firstly, we need to calculate the load fluctuation of the physical machine and divide the load fluctuation into three parts. Furthermore, the corresponding mathematical model predicts the load according to the different classification categories. Then, the predicted load value is used to dynamically adjust the threshold to improve the virtual machine merge probability. Finally, the simulation experiment is carried out on the cloud computing simulation platform cloudsim plus. The experimental results show that the virtual machine energy-saving merging method based on the three-way decision proposed in this paper can better reduce the number of migrations, increase the number of physical machines shut down, better improve the probability of virtual machine merger, and achieve the purpose of reducing the energy consumption of the data center.

Utilization prediction-based VM consolidation approach

Reducing energy consumption and optimizing resource usage in large cloud data centers is still an essential target for the current researchers and cloud providers. The state-of-the-art highlights the effectiveness of VM consolidation and live migrations in achieving reasonable solutions. However, most proposals consider only the real-time workload variations to decide whether a host is overloaded or underloaded, or to trigger migration actions. Such approaches may apply frequent and needless VM migrations leading to energy waste, performance degradation, and service-level agreement (SLA) violations. In this paper, we propose a consolidation approach based on the resource utilization prediction to determine the overloaded and underloaded hosts. The prediction method combines a Kalman filter and support vector regression (SVR) to forecast the host's future CPU utilization. Simulations are conducted on Cloudsim using real PlanetLab workloads to verify the performance of our proposal against existing benchmark algorithms. Experimental results demonstrate that our consolidation technique significantly reduces the SLA violation rate, number of VM migrations, and energy consumed in the datacenter.

Resource-Efficient Pareto-Optimal Green Scheduler Architecture

Rapidly developing cloud technology with enormous number of clients creates need of reducing power consumption of data centers. VM live migration is the most promising tool to achieve resource consolidation but it creates overheads in terms of additional CPU, disk I/O and network bandwidth utilization. This paper proposes a power-aware VM live migration based dynamic VM consolidation mechanism that focuses on reduction in datacenter’s resource utilization. Proposed mechanism is Pareto Optimal because during live migration it not only optimize the migration overheads but also select the VM and destination server by considering all the performance overheads to be generated during and after live migration. The proposed algorithm reduces nearly 60% of the VMs migration overheads. In terms of energy saving the proposed mechanism is 43% more efficient than the greedy scheduling approach and about 47% more energy efficient than the round-robin approach and thus achieves green computing goal.

Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers

Cloud Computing services can be accessed anytime, anywhere via the Internet. The overwhelming growth of cloud data centers over the past decade has increased their costs as energy demands have risen. As a result, higher carbon dioxide emissions and other greenhouse gasses are putting a strain on our ecosystem. The main objective of this study is to reduce the power consumption in cloud computing with no or negligible trade-offs in quality of service. This paper presents a new algorithm called the energy efficiency heuristic using virtual machine consolidation to minimize the high energy consumption in the cloud. By setting two thresholds, hosts are classified into three main classes. The designed model reallocates virtual machines from one physical host to another to minimize energy consumption. The results of the proposed algorithm have been obtained in terms of virtual machine migrations, performance degradation caused by migration, service level agreement violations, and execution time, showing a significant improvement over state-of-the-art techniques.

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

AbstractRecently, cloud computing is growing rapidly and contributing to the realization of other technologies. Cloud data centers use a lot of energy to host applications, which leads to high operating costs and increased carbon dioxide emissions into the environment. Dynamic consolidation of virtual machines (VMs) into the minimum number of physical servers is an efficient approach to managing energy consumption in cloud. Virtual machines placement (VMP) is an important problem in the proper consolidation of VMs and is the most common way to improve resource utilization. In VMP problem, VMs are consolidated to minimize the number of active physical servers. This consolidation is done using migration of VMs. After placement, the VMs execute on selected physical servers and the underloaded physical servers are off to optimize energy consumption. Basically, the purpose of VMP is to allocate a set of VMs to a subset of physical servers, taking into account some objectives such as reducing energy consumption and violating the service level agreement (SLA). In this article, an improved teaching‐learning based optimization (TLBO) algorithm is formulated to solve VMP as a multi‐objective problem, which we call VMP‐TLBO. VMP‐TLBO can perform optimal placement of migrated VMs to physical servers with the purpose of reducing energy consumption and SLA violation. VMP‐TLBO is implemented in MATLAB and the experimental results show that proposed algorithm does not violate SLA and compared to the best equivalence algorithm (ie, PAPSO), it improves energy consumption and SLA violation by 1.8% and 5.6%, respectively. In addition, VMP‐TLBO consolidates migrated VMs into the minimum number of physical servers to minimize the number of overloaded hosts as much as possible.

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers.

The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics.

Research advances on AI-powered thermal management for data centers

Given the complex nature of data centers' thermal management, which costs too many resources, processing time, and energy consumption, thermal awareness and thermal management powered by artificial intelligence (AI) are the targeted study. In addition to a few research on AI techniques and models, other strategies have also been introduced in recent years. Data center models, including cooling, thermal, power, and workload models, and their relationship are factors that need to be understood in the optimal thermal management system. Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines (VMs), hotspot identification, thermal state estimation, and power usage change. AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening. At present, research on AI-powered thermal control is still in its infancy. This paper concludes with four issues in thermal management, which will be the scope of further research.

Energy-Efficient Scientific Workflow Scheduling Algorithm in Cloud Environment

Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.

VM consolidation improvement approach using heuristics granular rules in cloud computing environment

Since cloud computing has been developing as a successful business and growing interest of organizations to make use of the environment, the volume of provided services is highly demanding. Cloud computing environment has high maintenance costs, therefore, the resources are limited; due to rapid progress and the highly demanded volume of utilization of resources, ascertaining an appropriate approach for controlling and managing resources has become essential in this area. Therefore, an algorithm must be designed to perform better in all QoS. A significant factor in current exploratory methods is that each method owns distinctive features and perhaps does not estimate all QoSes. For example, one specific method has low energy consumption but does not consider SLA violation. Concerning the aims of the present study, the granular model is designed which uses various criteria involved in computations; in practice, by entering the values given in the past and discovering the relationships among them, the most desirable classification is produced. The membership function and the inference rule are derived from the data provided by the actual workload. Experimental analysis of the results showed that this method works out better than all other related methods and avails consistent performance in all QoS criteria.

A high-applicability heterogeneous cloud data centers resource management algorithm based on trusted virtual machine migration

With the continuous development and maturity of cloud computing technology, the scale and number of cloud data center (CDC) are also expanding. This increasingly draws attention to the problem of high energy consumption in CDCs. Dynamic virtual machine (VM) consolidation is a promising approach for reducing energy consumption. VM migration, as a VM consolidation technology, can effectively improve the utilization of physical machine (PM) and optimize the scheduling process of CDCs. However, most VM integration algorithms, in existing research, are aimed at improving the utilization of PMs. Excessive utilization of PMs may increase the competition for shared resources among the VMs running on them. As a result, the performance of these VMs deteriorates, and the execution time of cloud tasks is increased or even interrupted. This study systematically analyzes the overall architecture of CDCs. Subsequently, migration rules are established for the one-dimensional and multidimensional trusted VMs. A high- applicability heterogeneous CDC resource management algorithm based on trusted VM migration (HTVM2) is then proposed. The proposed algorithm not only solves the one-dimensional VM migration problem of homogeneous and heterogeneous CDCs but also those of multi-dimensional VMs. This improves the success rate of VM migration, reduces the energy consumption of the CDC, and improves load balancing while ensuring VM performance. Finally, the algorithm was compared with the other three algorithms outperforming them all, as demonstrated by experimental results.

Спеціалізоване програмне забезпечення для моделювання динамічної консолідації віртуальних машин

For many cloud service providers, virtual machines remain the basic technology for computing virtualization. Virtual machines are used both to host application software and to implement container virtualization. Widespread use of virtual machines develops specialized software to determine the impact of model parameters on the quality of the consolidation process, which will prevent experimental research in production to evaluate new cloud data center resource management strategies. In recent years, there were many approaches in literature that offers various sets of software tools and frameworks for modeling data center processes, providing a platform and the necessary building blocks to optimize the process of consolidation of virtual machines. Models and software tools for modeling data center resource management processes are usually not exhaustive and solve a specific problem or management task. The specialized simulation software presented in the paper allows to investigate different control modes of virtual machines dynamic consolidation, provides a wide range of logging and debugging information using text and MS Excel files, such as performance indicators and workload diagrams, and allows to determine the optimal model parameters for various modes of data center operation, minimizing the number of active physical servers and reducing the number of SLA violations.

I-Neat: An intelligent framework for adaptive virtual machine consolidation

With the increasing use of cloud computing, high energy consumption has become one of the major challenges in cloud data centers. Virtual Machine (VM) consolidation has been proven to be an efficient way to optimize energy consumption in data centers, and many research works have proposed to optimize VM consolidation. However, the performance of different algorithms is related with the characteristics of the workload and system status; some algorithms are suitable for Central Processing Unit (CPU)-intensive workload and some for web application workload. Therefore, an adaptive VM consolidation framework is necessary to fully explore the potential of these algorithms. Neat is an open-source dynamic VM consolidation framework, which is well integrated into OpenStack. However, it cannot conduct dynamic algorithm scheduling, and VM consolidation algorithms in Neat are few and basic, which results in low performance for energy saving and Service-Level Agreement (SLA) avoidance. In this paper, an Intelligent Neat framework (I-Neat) is proposed, which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the system. The scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection algorithms. The algorithm library is designed based on a template, and in addition to the algorithms of Neat, I-Neat adds six new algorithms to the algorithm library. Furthermore, the framework manager helps users add self-defined algorithms to I-Neat without modifying the source code. Our experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.

Energy‐aware virtual machine consolidation based on evolutionary game theory

AbstractCloud data center consumes enormous amount of electrical power resulting in high operating cost and carbon dioxide emission. Virtual machine (VM) consolidation strives to reallocate VMs to the minimum number of physical machines dynamically to reduce energy consumption. An approach is proposed to solve consolidation problem of VMs toward optimization of their energy consumption. A novel algorithm is presented, and an energy consumption model is also presented to support the algorithm to find optimal solutions. Experimental results show that energy consumption can be reduced greatly by dynamic VM consolidation. In comparison with other classic algorithms, our proposed algorithm outcomes them and achieves average savings of 42% on energy consumption.

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

AbstractIn cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy‐aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi‐objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

Virtual Machine Consolidation using Roulette wheel selection Strategy

Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. Virtual Machine [VM] consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/under load detection, VM selection and VM placement. In Our Proposed Model We are going to use Roulette-Wheel Selection Strategy, Where the VM selects the Instance type and Physical Machine [PM] using Roulette-Wheel Selection Mechanism Keywords—searchable encryption, dynamic update, cloud computing