Dynamic VM Consolidation Research Articles

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

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.

Efficient HPC and Energy-Aware Proactive Dynamic VM Consolidation in Cloud Computing

The adoption of High-Performance Computing (HPC) applications has gained an extensive interest in the Cloud computing. Current cloud vendors utilize separate management tools for HPC and non-HPC applications, missing out on the consolidation benefits of virtualization. Non-HPC applications executed in the cloud may interfere with resource-hungry HPC applications, which is a key performance challenge. Furthermore, correlations between application major performance indicators, such as response time and throughput, with resource capacities reveal that conventional placement strategies are impacting virtual machine efficiency, resulting in poor resource optimization, increased operating expenses, and longer wait times. Since applications often underutilized the hardware, smart execution of HPC and Non-HPC applications on the same node can boost system and energy efficiency. This research incorporates proactive dynamic VM consolidation to enhance the resource usage and performance while maintaining energy efficiency. The proposed algorithm generates a workload-aware fine-grained classification by employing machine learning techniques to generate complimentary profiles that alleviate cross-application interference by intelligently co-locating non-HPC and HPC applications. The research used CloudSim to simulate real HPC workloads. The results verified that the proposed algorithm outperforms all heuristic methods with respect to the metrics in key areas.

An efficient energy-aware approach for dynamic VM consolidation on cloud platforms

An efficient energy-aware approach for dynamic VM consolidation on cloud platforms

Dynamic Consolidation of Virtual Machine: A Survey of Challenges for Resource Optimization in Cloud Computing

Background:: Virtualization is an efficient technology that accelerates available data center to support efficient workload for the application. It completely based on guest operating system which keeps track of infrastructure that keeps track of real time usage of hardware and utilization of software. Objective:: To address the issues with Virtualization this paper analyzed various virtualization terminology for treating best effective way to reduce IT expenses while boosting efficiency and deployment for all levels of businesses. Methods: This paper discusses about the scenarios where various challenges met by Dynamic VM consolidation. Dynamic conclusion of virtual machines has the ability to increase the consumption of physical setup and focus on reducing power utilization with VM movement for stipulated period. Gathering the needs of all VM working in the application, adjusting the Virtual machine and suitably fit the virtual resource on a physical machine. Profiling and scheduling the virtual CPU to another Physical resource. This can be increased by making live migration with regards to planned schedule of virtual machine allotment. Results:: The recent trends followed in comprehending dynamic VM consolidation is applicable either in heuristic-based techniques which has further approaches based on static as well as adaptive utilization threshold. SLA with unit of time with variant HOST adoption (SLATAH) which is dependent on CPU utilization threshold with 100% for active host. Conclusion:: The cloud provider decision upon choosing the virtual machine for their application also varies with their decision support system that considers data storage and other parameters. It is being compared for the continuous workload distribution as well as eventually compared with changing demands of computation and in various optimization VM placement strategies.

Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers

In the age of the information explosion, the energy demand for cloud data centers has increased markedly; hence, reducing the energy consumption of cloud data centers is essential. Dynamic virtual machine VM consolidation, as one of the effective methods for reducing energy energy consumption is extensively employed in large cloud data centers. It achieves the energy reductions by concentrating the workload of active hosts and switching idle hosts into low-power state; moreover, it improves the resource utilization of cloud data centers. However, the quality of service (QoS) guarantee is fundamental for maintaining dependable services between cloud providers and their customers in the cloud environment. Therefore, reducing the power costs while preserving the QoS guarantee are considered as the two main goals of this study. To efficiently address this problem, the proposed VM consolidation approach considers the current and future utilization of resources through the host overload detection (UP-POD) and host underload detection (UP-PUD). The future utilization of resources is accurately predicted using a Gray-Markov-based model. In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee.

A Heuristic Research of the Designing and Evaluation of Energy Efficiency using Dynamic VM Consolidation in Green Cloud Data Centres

Allotted computing is a blasting innovation that tenders effective assets, and smooth accessibility through web based processing. however, the growing wishes of clients for such administrations are convincing the cloud professional corporations to send huge portions of strength hungry server farms which element awful effect to the earth with the aid of the usage of plenteous Carbon Dioxide discharge. To limit control usage and strengthen the quality of service (QoS) inside the server farm assesses the strength usage in an assortment of plans in IaaS of dispensed computing situation. Dynamic Virtual Machines’ Consolidation and Placement(DVMCP) is an in a position strategies for enhancing using assets and proficient power usage in Cloud DataCenters. in this exploration, we proposed a calculation, Energy Conscious Greeny Cloud Dynamic (ECGCD) set of rules that accomplishes live VM relocation that is turning off the inert has or located it to lowcontrol mode (i.e., rest or hibernation),that builds up power productivity and succesful usage of property in the dynamic hosts. The take a look at stop result confirmations with duplicate that, the proposed calculation achieves good sized diploma of lower in electricity usage in correlation with the modern-day-day VM combination calculations.

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Minimizing the energy consumption and resource usage in cloud computing environment is one of the key research issues. Energy aware resource allocation is used to optimize the power consuming by computer resources and storage in cloud. The proposed system is to improve the utilization of computing resources and reduce energy consumption under workload independent quality of service constraints. Using migration for minimizing the number of active physical nodes the dynamic single threshold VM consolidation leverages fine-grained fluctuations in the workloads and continuously reallocates VMs . A genetic algorithm based power-aware scheduling of resource allocation (G-PARS) has been proposed to solve the dynamic virtual machine allocation policy problem. The experiment results show that strategy that has been proposed has a better performance than other strategies, not only in high Quality Of Service(QoS) but also in less energy consumption.

Erratum to: A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

Erratum to: A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

A dynamic VM consolidation technique for QoS and energy consumption in cloud environment

Cloud-based data centers consume a significant amount of energy which is a costly procedure. Virtualization technology, which can be regarded as the first step in the cloud by offering benefits like the virtual machine and live migration, is trying to overcome this problem. Virtual machines host workload, and because of the variability of workload, virtual machines consolidation is an effective technique to minimize the total number of active servers and unnecessary migrations and consequently improves energy consumption. Effective virtual machine placement and migration techniques act as a key issue to optimize the consolidation process. In this paper, we present a novel virtual machine consolidation technique to achieve energy–QoS–temperature balance in the cloud data center. We simulated our proposed technique in CloudSim simulation. Results of evaluation certify that physical machine temperature, SLA, and migration technique together control the energy consumption and QoS in a cloud data center.

FuMAEVMS Novel VM Selection in Dynamic Consolidation of Virtual Machine

Background/Objective: Dynamic VM consolidation has been known as one from several ways to reduce energy consumption in cloud data center. The problem in reducing energy consumption with dynamic VM Consolidation has recognize as NP-Hard problem that divided into four subs, where VM selection is one of sub-problem. The objective VM selection is to choose the best VM that suitable to move from overload host and avoid oversubscription host. Methods: This research proposed novel VM selection model based on Fuzzy Logic, Markov Normal Algorithm (MA) and Existing VM Selection (FuMAEVMS) in dynamic VM consolidation. Moreover, RAM VM and max MIPS VM are consider as attribute to be used. The proposed VM selection model have been evaluated using Cloudsim with datasets from PlanetLabs in various condition of VM instance. The research measured the performance using several parameters such as Energy Consumption (EC), SLA Violation (SLAV), SLA Time per active host (SLATAH) and Performance Degradation Due Migration (PDM). FuMAEVMS were compared with existing VM selection such asconstant first selection (CFS), minimum migration time (MMT), random choice (RC) and maximum correlation (MC). Findings: FuMAEVMS as Novel VM selection capable to decide which VM that should be migrate from overload host in various condition VM instances that gives improvement of energy efficiency in cloud data center. We analyze the used of fuzzy logic in categorizing attribute VM capable to help in minimize rules production in Markov Normal Algorithm. This condition gives benefit to MA in deciding suitable existing VM selection such as CFS, MMT or MC to be used in migrating VM. Improvement: Results experiment has showed FuMAEVMS VM selection capable to reduced energy consumption in cloud data center significantly up to 4.45% when applied in dynamic VM consolidation compared with RC, MMT and MC.

Autonomous Migration of Virtual Machine Based on OpenStack

For the problem of compute nodes load unbalance leads to the overload node impact VM performance and the underload node waste electrical energy. Dynamic VM consolidation is an efficient approach for improving the utilization of servers and reducing energy costs. In this paper, we introduce dynamic consolidation into OpenStack and implement AMVM based on OpenStack. AMVM collect and analyze all performance data of VM and compute node in OpenStack. Then AMVM automatically schedules VM live migrate to the most appropriate node according to the real-time load of all compute nodes. Then we use hybrid-copy instead of pre-copy in AMVM to improve the efficiency of VM live migration. Finally, we analyze the performance of hybrid-copy, hybrid-copy can reduce page faults compared with post-copy and reduce total data transferred compared with pre-copy.

Energy Efficient Scheduling of Cloud Application Components with Brownout

It is common for cloud data centers meeting unexpected loads like request bursts, which may lead to overloaded situation and performance degradation. Dynamic Voltage Frequency Scaling and VM consolidation have been proved effective to manage overloads. However, they cannot function when the whole data center is overloaded. Brownout provides a promising direction to avoid overloads through configuring applications to temporarily degrade user experience. Additionally, brownout can also be applied to reduce data center energy consumption. As a complementary option for Dynamic Voltage Frequency Scaling and VM consolidation, our combined brownout approach reduces energy consumption through selectively and dynamically deactivating application optional components, which can also be applied to self-contained microservices. The results show that our approach can save more than 20% energy consumption and there are trade-offs between energy saving and discount offered to users.

Improvement of Energy Efficiency at Cloud Data Center Based on Fuzzy Markov Normal Algorithm VM Selection in Dynamic VM Consolidation

The problem of the energy consumption in cloud data center has become an important study in recent years. Dynamic VM consolidation has been known as a solution able to reduce energy consumption in cloud data center. However, there are many problems in Dynamic VM consolidation, in particular the VM selection. The objective of VM selection is to choose VM candidates suitable to move from overload host for avoiding oversubscribed host and to give an impact for the reduction of energy consumption. This research proposes a VM selection model in dynamic VM consolidation to improve the energy efficiency in cloud data center based on Fuzzy Markov Normal Algorithm. Fuzzy logic has been used for categorizing the attributes of VM candidates. After that, Markov Normal Algorithm has been used for deciding to which category VM should be migrated from overload host. The proposed VM selection model has been evaluated using various VM instance conditions (homogeneous or heterogeneous) with datasets from PlanetLabs in Cloudsim. Moreover, several parameters were used to measure the performance in this research such as Energy Consumption, SLA Violation, SLA Time per active host, and Performance Degradation Due Migration. The results experiments have shown the proposed VM selection model capable of improving energy efficiency in cloud data center up to 3.74%, 6.65%, 5.36%, and 5.11%, compared with the existing VM selections such as Constant First Selection, Minimum Migration Time, Random Choice, and Maximum Correlation.

Pervasive Cloud Controller for Geotemporal Inputs

The rapid cloud computing growth has turned data center energy consumption into a global problem. At the same time, modern cloud providers operate multiple geographically-distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures. Furthermore, to account for emerging technologies surrounding the cloud ecosystem, a maintainable control solution needs to be forward-compatible. Existing cloud controllers are focused on VM consolidation methods suitable only for a single data center or consider migration just in case of workload peaks, not accounting for all the aspects of geographically distributed data centers. In this paper, we propose a pervasive cloud controller for dynamic resource reallocation adapting to volatile time- and location-dependent factors, while considering the QoS impact of too frequent migrations and the data quality limits of time series forecasting methods. The controller is designed with extensible decision support components. We evaluate it in a simulation using historical traces of electricity prices and temperatures. By optimising for these additional factors, we estimate 28.6% energy cost savings compared to baseline dynamic VM consolidation. We provide a range of guidelines for cloud providers, showing the environment conditions necessary to achieve significant cost savings and we validate the controller's extensibility.

Evaluation of Selection Policy with Various Virtual Machine Instances in Dynamic VM Consolidation for Energy Efficient at Cloud Data Centers

Various VM instances in Cloud Infrastructure provide flexibility for user to meet their computation requirements. However, this condition leads to the complex infrastructure that require numerous resources and consumes massive electricity due to the flexibility of VM instances. This paper concern in evaluate VM selection policy in Dynamic VM Consolidation. The study would evaluate our proposed method Constant Position Selection Policy (CPS) that compared with other VM Selection Policy such as Minimum Migration Time (MMT),Random Choice(RC) and Maximum Correlation (MC).Evaluation process of this study, measured the performance of Energy Consumption, SLAV, SLATAH, and PDMwith real workload trace data from PlanetLab VMs in various VM instances. Result the proposed method able to minimizing energy consumption of cloud data center in various VM instances with acceptable SLA

Workload Known VMM Scheduler for Server Consolidation for Enterprise Cloud Data Center

This study proposes a novel adaptive meta-heuristics based scheduling policies for provisioning the VCPU resources among competing VM service domains in a cloud. Such provisioning guarantees to Service Level Agreement for each domain, with respect to the diverse workloads on-the-fly. The framework is built on CSIM models and tools, making it easy to understand and configure various virtualization setups. The study demonstrates the usefulness of the framework by evaluating proactive, reactive and adaptive VCPU scheduling algorithms. The paper evaluates how periodic/aperiodic execution of control actions can affect policy performance and speed of convergence. The periodic reactive resource allocation is used as the baseline for analysis and the average response time is the performance metric. Simulation based experiments using variety of real-world arrival traces and synthetic workloads results that the proposed provisioning technique detects changes in arrival pattern and resource demands and allocates resources accordingly to achieve application SLA targets. The proposed model improves CPU utilization and makes the best tradeoff between resource utilization and performance from 2 to 6% comparing with the default VMM scheduler configurations for diverse workloads. In addition, the results of the experiments show that the proposed Weighed Moving Average algorithm combined with the aperiodic policy significantly outperforms other dynamic VM consolidation algorithms in all cases, in regard to the SLA metric due to a substantially reduced level of response time violations and the frequency of algorithm invocation.

Power - Aware Virtual Machine Consolidation considering Multiple Resources with Live Migration

the computing technologies require high level of efficiency and infrastructure that datacenters were unable to provide previously. Lack of efficient infrastructure can serve only limited number of users and simultaneously emits large amount of carbon footprint and over utilizes electrical power. Virtualization emerged as prime technique for service management and reduced energy expenses at datacenter. Hence, there is a need of virtualization techniques that enhance energy efficiency and CPU utilization along with required user's QoS (Quality of Service) in terms of minimum response time and maximum throughput. We propose efficient virtualization technique for dynamic VM consolidation according to the current power consumption by considering multiple resources (CPU, ram, and storage disk) with live migration of VMs by switching idle server to sleep mode, thus minimizing energy consumption at datacenter. The proposed approach can effectively handle strict SLA (Service Level Agreement) in homogeneous as well as in heterogeneous environment. The result verifies that the proposed algorithm reduces the number of active physical machines and it results into reduction of power consumption.