Virtual Machine Consolidation Research Articles

MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

Fuzzy logic-based VM selection strategy for cloud environment

In dynamic virtual machine consolidation (DVMC), when a host is overloaded, a list of virtual machines (VMs) must be selected to migrate. VM selection strategy has a substantial impact on the performance of DVMC algorithm, and a good number of strategies have been proposed in the literature. As different VM selection approaches offer distinct advantages, we explore and apply fuzzy logic in this research to innovate a new VM selection method which will provide a balanced and better result in all quality of service (QoS) measures. With this goal, we have designed the fuzzy VM selection using three metrics. We have devised the membership functions and inference rules based on the real cloud workload data, e.g., PanetLab. We implement our novel fuzzy VM selection method in CloudSim toolkit and compare the performance with the existing methods. Simulation results demonstrate a balanced performance by trading off power and QoS parameters.

Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment

In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.

Dynamic Voltage and Frequency Scaling‐aware dynamic consolidation of virtual machines for energy efficient cloud data centers

SummaryComputational demand in data centers is increasing because of the growing popularity of Cloud applications. However, data centers are becoming unsustainable in terms of power consumption and growing energy costs so Cloud providers have to face the major challenge of placing them on a more scalable curve. Also, Cloud services are provided under strict Service Level Agreement conditions, so trade‐offs between energy and performance have to be taken into account. Techniques as Dynamic Voltage and Frequency Scaling (DVFS) and consolidation are commonly used to reduce the energy consumption in data centers, although they are applied independently and their effects on Quality of Service are not always considered. Thus, understanding the relationship between power, DVFS, consolidation, and performance is crucial to enable energy‐efficient management at the data center level. In this work, we propose a DVFS policy that reduces power consumption while preventing performance degradation, and a DVFS‐aware consolidation policy that optimizes consumption, considering the DVFS configuration that would be necessary when mapping Virtual Machines to maintain Quality of Service. We have performed an extensive evaluation on the CloudSim toolkit using real Cloud traces and an accurate power model based on data gathered from real servers. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 41.62% for scenarios under dynamic workload conditions. These outcomes outperforms previous approaches, that do not consider integrated use of DVFS and consolidation strategies.

Bayesian network-based Virtual Machines consolidation method

Efficient Virtual Machines (VMs) consolidation, as one of the primary methods for balancing between guaranteeing Quality of Service (QoS) and saving energy, is critical for data centers. Most existing VMs consolidation methods reallocate physical resources by adopting live VM migration. Therefore, VMs consolidation can be cast into estimating the physical resource utilization in Physical Machines (PMs) and predicting the migration probability of VMs. In this paper, we develop a Bayesian network-based estimation model (BNEM) for live VM migration, allowing a comprehensive treatment of nine actual factors in real data centers. A selection criterion of VMs to be migrated and a VM placement criterion are presented. By combining three algorithms corresponding to different phases in VMs consolidation, a hybrid Bayesian network-based VMs consolidation (BN-VMC) method is proposed. We have validated our approach by conducting a performance evaluation study using CloudSim toolkit, and the trace-driven comparison experiments are also performed. The simulation results show that the method can significantly degrade energy consumption, avoid inefficient VM migrations, and improve QoS.

E-eco: Performance-aware energy-efficient cloud data center orchestration

The high energy consumption of data centers has been a recurring issue in recent research. In cloud environments, several techniques are being used that aim for energy efficiency, ranging from scaling the processors frequency, to the use of sleep states during idle periods and the consolidation of virtual machines. Although these techniques enable a reduction in power consumption, they usually impact application performance. In this paper, we present an orchestration of different energy-savings techniques in order to improve the trade-off between energy consumption and application performance. To this end, we implemented the Energy-Efficient Cloud Orchestrator - e-eco - a management system that acts along with the cloud load balancer deciding which technique to apply during execution. To evaluate e-eco, tests were carried out in a real environment using scale-out applications on a dynamic cloud infrastructure, taking into account transactions per second as a performance metric. In addition to the empirical experiments, we also analyzed the scalability of our approach with an enhanced version of the CloudSim simulator. Results of our evaluations demonstrated that e-eco is able to reduce energy consumption up to 25% compared to power-agnostic approaches at a cost of only 6% of extra SLA violations. When compared to existing power-aware approaches, e-eco achieved the best trade-off between performance and energy-savings. These results showed that our orchestration approach showed a better balance in regard to a more energy-efficient data center with smaller impact on application performance when compared with other works presented in the literature.

Multi-Capacity Combinatorial Ordering GA in Application to Cloud resources allocation and efficient virtual machines consolidation

This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given.

A Taxonomy and Survey of Power Management Strategies in Cloud Data Centers

In recent years, Cloud computing has obtained popularity to offer utility-oriented IT services to global users over the internet. Cloud computing is a paradigm to develop scalable on-demand virtualized resources based on a pay-as-you-go model. Due to the rapid growth of the cloud services and technologies, a serious concern for cloud data centers is a huge amount of energy consumption. This paper provides an overview of the initial challenges for the energy consumption of data centers under QoS constraints in Cloud environments. Then, taxonomy of existing solutions along with their most typical features, benefits and challenges are presented. This taxonomy comprises the core approaches and techniques in energy-efficiency in cloud data centers, emphasizing on VM consolidation algorithms.

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.

M-Convex VM Consolidation: Towards a Better VM Workload Consolidation

Consolidating virtual machine workload is a unique feature of cloud computing platforms that greatly reduces the operating cost of the cloud data center. Correctly consolidating VMs’ workloads for a large scale cloud computing platform is nontrivial because a shortsighted scheme may save some cost in one aspect but becomes expensive in other aspects being neglected. In this paper, we present a framework that automates the VM consolidation process to improve the VMs and servers assignment whenever such improvement is possible. The proposed VM consolidation framework can achieve a balance among multiple administrative objectives (e.g., power cost, network cost) during the VM consolidation process. The solution method of solving the VM consolidation problem is designed based on the powerful and efficient semi quasi M-convex optimization framework. The proposed algorithm can also produce VM consolidation solutions that require minimal system reconfigurations (e.g., VM migrations, turning on/off servers). More importantly, the proposed algorithm can be implemented distributedly so that the scalability of the proposed framework is greatly improved. As a result, the proposed framework is efficient, scalable and highly practical.

Optimized Energy Efficient Virtual Machine Placement Algorithm and Techniques for Cloud Data Centers

Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that's why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. Hence there is need for green computing and for energy efficient management of cloud data center resources besides meeting the QoS constraints. Within a data center, maximum energy is consumed by cooling systems and ICT Infrastructure, particularly, the servers. So it is of utmost importance to optimize energy utilization in datac enter servers. In order to achieve this, we can leverage the power of virtualization which is intrinsic in Cloud Computing. Virtualization opens the doors for VM consolidation by allowing dynamic migration of virtual machines across physical machines. In this study, the problem of VM consolidation has been formulated as a mathematical optimization problem.

A Heuristic Time Sharing Policy for Backup Resources in Cloud System

Cloud computing promises high performance and cost-efficiency. However, most cloud infrastructures operate at a low utilization, which greatly adheres cost effectiveness. Previous works focus on seeking efficient virtual machine (VM) consolidation strategies to increase the utilization of virtual resources in production environment, but overlook the under-utilization of backup virtual resources. We propose a heuristic time sharing policy of backup VMs derived from the restless multi-armed bandit problem. The proposed policy achieves increasing backup virtual resources utilization and providing high availability. Both the results in simulation and prototype system experiments show that the traditional 1:1 backup provision can be extended to 1:M (M≫1) between the backup VMs and the service VMs, and the utilization of backup VMs can be enhanced significantly.

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.

WAYFINDER: parallel virtual machine reallocation through A* search

Modern virtual machine (VM) management software enables consolidation of VMs for power savings or load-balancing for performance. While existing literature provides various methods for computing a better load-balanced, or consolidated goal state, it fails to adequately suggest the best path from the system’s current state to the desired goal allocation. This paper discusses an approach to efficient path finding in VM placement problems for cloud computing environments of moderate scale with results indicating the solution is reasonable for managing hundreds of VMs. We present an overview of known approaches to dynamic VM placement and discuss their shortcomings with respect to dynamic reallocation. We then describe a novel design and implementation of a heuristic search algorithm to determine optimal sequential migration plans to transition from a given VM-to-host allocation to an arbitrary desired allocation state. We then elaborate nuances of A* application to this domain along with our simulation-based validation approach. Finally, this work demonstrates a novel and highly effective technique for exploiting migration parallelism in order to rapidly achieving VM reallocation convergence suitable for continual workload rebalancing in cloud data centers.

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Reducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO $$ _2 $$ footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.

VM consolidation approach based on heuristics, fuzzy logic, and migration control

To meet the increasing demand of computational power, at present IT service providers' should choose cloud based services for its flexibility, reliability and scalability. More and more datacenters are being built to cater customers' need. However, the datacenters consume large amounts of energy, and this draws negative attention. To address those issues, researchers propose energy efficient algorithms that can minimize energy consumption while keeping the quality of service (QoS) at a satisfactory level. Virtual Machine consolidation is one such technique to ensure energy-QoS balance. In this research, we explore fuzzy logic and heuristic based virtual machine consolidation approach to achieve energy-QoS balance. A Fuzzy VM selection method is proposed in this research. It selects VM from an overloaded host. Additionally, we incorporate migration control in Fuzzy VM selection method that will enhance the performance of the selection strategy. A new overload detection algorithm has also been proposed based on mean, median and standard deviation of utilization of VMs. We have used CloudSim toolkit to simulate our experiment and evaluate the performance of the proposed algorithm on real-world work load traces of Planet lab VMs. Simulation results demonstrate that the proposed method is most energy efficient compared to others.

Energy efficiency of VM consolidation in IaaS clouds

The energy efficiency of cloud computing has recently attracted a great deal of attention. As a result of raised expectations, cloud providers such as Amazon and Microsoft have started to deploy a ...

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.

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Abstract The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service (QoS) oriented computing platform. To meet these requirements, virtualization and Virtual Machine (VM) consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm (A-GA) based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time (MMT), Maximum Correlation (MC) and Random Selection (RS), along with different CPU utilization estimation approaches like Inter Quartile Range (IQR), Local Regression (LR), Local Robust Regression (LRR), static THReshold (THR) and Median Absolute Deviation (MAD) has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization (ACO) and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement (SLA) violation and QoS assurance is inevitable.