Energy Efficient Virtual Machine Research Articles

DBSCAN based approach for energy efficient VM placement using medium level CPU utilization

Virtual machine placement (VMP) is a popular problem in Cloud Data Centers (CDCs). An efficient virtual machine (VM) allocation is essential for processor speed and energy saving. This is more useful where the CDC uses an Internet of Things (IoT) infrastructure. To enhance energy savings, we aim to improve the adaptive four thresholds energy-aware framework for VM deployment. We observed that the role of the threshold for identifying the over-loaded host is crucial. In order to determine the appropriate threshold, we employed density-based spatial clustering of applications with noise (DBSCAN), medium absolute deviation (MAD), and interquartile range (IQR) using the medium fit power efficient decreasing (MFPED) algorithm. Our proposed algorithm modified medium fit energy efficient decreasing (MMFEED) achieves a reduction in energy consumption of 47.3%, 46.1%, 39%, 23.2%, 10.9%, and 3.4% compared to the IQR, MAD, static threshold (THR), exponential weighted moving average (EWMA), modified energy-efficient virtual machine placement (MEEVMP), and adaptive four threshold energy-aware framework for VM deployment energy efficient (AFED-EF), respectively, under the minimum migration time (MMT) selection policy. The proposed algorithm outperforms these algorithms in terms of energy consumption for VM selection policy MMT.

Energy-aware virtual machine placement based on a holistic thermal model for cloud data centers

As energy-intensive infrastructures, data centers (DCs) have become a pressing challenge for managers due to their significant energy consumption and carbon emissions. Information technology (IT) and cooling systems contribute the most to energy consumption. Energy-aware virtual machine (VM) scheduling methods have been widely demonstrated to reduce energy consumption and operating costs in DCs. However, as realistic DCs exhibit complex power and thermodynamic behaviors, existing works cannot provide efficient measures to optimize computing and cooling power consumption simultaneously. To overcome this challenge, we construct a holistic thermal model (including CPU and server inlet thermal models) to accurately represent the non-uniform, dynamic thermal environment. Subsequently, this work proposes a thermal model-based energy-aware VM placement method (TEVP) to minimize the holistic energy consumption of the DCs, considering resource and thermal constraints. We develop a novel hybrid swarm intelligence algorithm (DE-ERPSO) combining differential evolution (DE) and particle swarm optimization with an elite re-selection mechanism (ERPSO) to explore more energy-efficient VM placement schemes. Extensive experiments are conducted on an extended CloudSim to validate the performance of the proposed TEVP using real-world workload traces (PlanetLab and Azure). Results show that TEVP saves over 5.6% of the total energy consumption over the advanced baselines while maintaining low thermal violations.

Energy-efficient virtual machine placement in heterogeneous cloud data centers: a clustering-enhanced multi-objective, multi-reward reinforcement learning approach

Energy-efficient virtual machine placement in heterogeneous cloud data centers: a clustering-enhanced multi-objective, multi-reward reinforcement learning approach

SCRUB: a novel energy-efficient virtual machines selection and migration scheme in cloud data centers

SCRUB: a novel energy-efficient virtual machines selection and migration scheme in cloud data centers

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds.

Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers.

Energy-efficient virtual machine scheduling in IaaS cloud environment using energy-aware green-particle swarm optimization

Energy-efficient virtual machine scheduling in IaaS cloud environment using energy-aware green-particle swarm optimization

A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers

The traditional method of saving energy in Virtual Machine Placement (VMP) is based on consolidating more virtual machines (VMs) in fewer servers and putting the rest in sleep mode, which may lead to the overheating of servers resulting in performance degradation and cooling cost. The lack of an accurate and computationally efficient model to describe the thermal condition of the data center environment makes it challenging to develop an effective and adaptive VMP mechanism. Although recently, data-driven approaches have acted successfully in model construction, the shortage of clean, adequate, and sufficient amounts of data put limits their generalizability. Moreover, any change in the data center configuration during operation, makes these models prone to error and forces them to repeat the learning process. Thus, researchers turn to applying model-free paradigms such as reinforcement learning. Due to the vast action-state space of real-world applications, scalability is one of the significant challenges in this area. In addition, the delayed feedback of environmental variables such as temperature give rise to exploration costs. In this paper, we present a decentralized implementation of reinforcement learning along with a novel state-action representation to perform the VMP in the data centers to optimize energy consumption and keep the host temperature as low as possible while satisfying Service Level Agreements (SLA). Our experimental results show more than 17% improvement in energy consumption and 12% in CPU temperature reduction compared to baseline algorithms. We also succeeded in accelerating optimal policy convergence after the occurrence of a configuration change.

Energy-efficient virtual machine placement algorithm based on power usage

Energy-efficient virtual machine placement algorithm based on power usage

Energy efficient resource optimization algorithm for cloud infrastructure

The proliferation of cloud computing infrastructure has increased the energy demand remarkably. Energy-efficient resource management is essential for running a cost effective and environment friendly data center. Virtual Machine (VM) consolidation is a well-accepted method for reducing the energy consumption of the cloud data center. Quality of service is an equally important aspect of cloud services. VM migrations caused by consolidation often cause degradation in QoS. These two parameters have been dealt with individually in most research and very few addressed both energy efficiency and QoS simultaneously. We have proposed a new Energy and QoS Efficient (EQSE) VM selection and placement method for improving the energy efficiency along with quality of service (QoS). VM selection and placement are two critical steps of VM consolidation. EQSE uses Resource Gap Minimization (RGM) algorithm for VM selection and Utilization-Aware Best-Fit Decreasing (UABFD) algorithm for placement of these VMs. EQSE along with dynamic thresholds reduces energy consumption and improves the quality of service by reducing the number of VM migrations. CloudSim simulation performed on PlanetLab data establishes the superiority of the proposed method compared to the existing state of the art methods of VM consolidation.

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

Autonomous DRL-based energy efficient VM consolidation for cloud data centers

The exponential data growth and demands for high computing resources lead to excessive resource use in cloud data centers, which cause an increase in energy consumption and high carbon emissions in the environment. So, the high energy consumption, inefficient resource usage, and quality of service assurance (QoS) are major challenges for cloud data centers. The dynamic consolidation of Virtual Machines (VMs) is proven to be an efficient way to tackle these issues while reducing energy consumption and improving resource utilization in data centers. It reduces the number of active hosts for energy efficiency by switching under-utilized and idle hosts into lower power mode. So, several heuristics and Artificial Intelligence (AI) based VM consolidation approaches have been published in papers. Most existing approaches rely on the aggressive consolidation of VMs for energy efficiency, thus causing performance degradation and high SLA violation. However, an automated solution is needed to reduce energy consumption and SLA violation by ensuring efficient resource usage in the cloud data center environment. Therefore, this article proposes an energy-efficient autonomous VM consolidation (AVMC) mechanism that has Deep Reinforcement Learning (DRL) based agent for performing VM consolidation decisions. The DRL agent learns the optimal distribution of VMs in the data center, considering energy efficiency and QoS assurance. The real-time workload traces from PlanetLab have been used to validate the proposed mechanism. Experimental results reveal the superiority of the proposed AVMC system over the existing models. AVMC reduced the energy consumption and SLA violation rate significantly.

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.

EVCT: An Efficient VM Deployment Algorithm for a Software-Defined Data Center in a Connected and Autonomous Vehicle Environment

Software-defined data centers (SDDC) are an emerging softwarized model that can monitor the virtual machines’ allocation atop the cloud servers. SDDC consists of softwarized entities like Virtual Machine (VM) and hardware entities like servers and connected switches. SDDCs apply VM deployment algorithms to preserve efficient placement and processing data traffic generated from the Connected and Autonomous Vehicles (CAV). To enhance user satisfaction, SDDC providers are always looking for an intellectual model to monitor large-scale incoming traffics, such as the Internet of Things (IoT) and CAV applications, by optimizing service quality and service level agreement (SLA). This paper is motivated by this, raising an energy-efficient VM cluster placement algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EVCT</i> to handle service quality and SLA issues in an SDDC in a CAV environment. EVCT algorithm leverages the similarity between VMs and models the problem of VM deployment into a weighted directed graph. Based on the amount of traffic between VM, EVCT adopts the “maximum flow and minimum cut theory” to cut the directed graph and achieve high energy-efficient placement for VMs. The proposed algorithm can efficiently reduce the energy consumption cost, provide a high quality of services (QoS) to users, and have good scalability for the variable workload. We have also carried out a series of experiments to use the real-world workload to evaluate the performance of the EVCT. The results illustrate that the EVCT surpasses the state-of-the-art algorithms in terms of energy consumption cost and efficiency.

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

A Study on Transfer Functions of Binary Particle Swarm Optimization for Energy-Efficient VM Placement

Cloud computing has emerged as the backbone of the IT industry for infrastructural support. In cloud computing, resources are virtualized in form of Virtual Machines which eventually is mapped to physical infrastructure. Energy-efficient virtual machine placement is an important problem in cloud computing and has attracted the attention of researchers in recent. As virtual machine placement is an NP-hard problem, meta-heuristics have often been applied vastly for this. In one of our earlier works, Modified Binary Particle Swarm Optimization algorithm has been applied for the VM placement. It was observed therein that the transfer function, which plays an important role to obtain an optima, does not completely avoid the problem of local optima. Therefore, in this work, we have studied the behavior of eight different transfer functions towards this property. For this, energy-efficient VM placement problem is modelled as multi-objective optimization problem and Binary Particle Swarm Optimization is applied. The study is done by simulation and their statistical analysis.

Energy-efficient virtual-machine mapping algorithm (EViMA) for workflow tasks with deadlines in a cloud environment

Processing large scientific applications generates a huge amount of data, which makes running experiments in the cloud computing environment very expensive and energy-consuming. To find an optimal solution to the workflow scheduling problem, several approaches have been presented for scheduling workflow on cloud resources. However, more efficient approaches are needed to improve cloud service delivery. In this paper, an energy-efficient virtual machine mapping algorithm (EViMA) is proposed to improve resource management in the cloud computing environment to achieve effective scheduling that reduces cloud data center energy consumption, execution makespan, and execution cost. This ensures that the requirements of cloud users are met, and improves the quality of services offered by cloud providers. Our proposed mechanism considers the heterogeneity of scheduling from both cloud users’ and workflow applications’ perspectives. Through simulation experiments on real workflow datasets, the proposed EViMA can provide better solutions for both cloud users and cloud providers by reducing energy consumption, execution makespan, and execution cost better than the state-of-the-art.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Energy efficiency is a critical issue in data centre management, which is the foundation for cloud computing. The VM placement has a considerable impact on a data centre's energy efficiency and resource utilisation. The assignment of VMs to PMs is an NP-hard problem without an easy way to find an optimal solution, particularly in large-scale data centres. In this study, the VM placement problem is formulated as a constrained optimisation problem. The Genetic Algorithm (GA) is a suitable method for solving this problem in terms of the quality of the solution. However, GA is time-consuming to obtain an optimal solution in the large scale optimisation problem. Therefore, this paper focuses on accelerated GA for energy-efficient VM placement. As the most time-consuming element of the GA is the calculation of its fitness function, this paper simplifies this calculation through a new fitness function in GA. Simulation results of small-, medium-, and large-scale data centres demonstrate that our accelerated GA is faster than the standard GA and gives better quality of solution than the First Fit Decreasing (FFD) algorithm, respectively. The findings of our GA with the new fitness function reveal an 8% energy saving for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA with standard energy formula as a fitness function. The number of generations in our GA is reduced by about 50% in comparison with the standard GA. Moreover, we started with 3000 PMs in the large-scale dataset, and only 1086 PMs were actually used after running our GA. Therefore, we may switch off far more PMs for energy savings from our GA results than those from the standard GA.

Look-ahead energy efficient VM allocation approach for data centers

Energy efficiency is an important issue for reducing environmental dissipation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient provisioning is addressed at the data center level in this research. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. Energy efficiency and performance are inversely proportional. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT.