Virtual Machine Consolidation Research Articles

An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.

An Efficient Representation Using Harmony Search for Solving the Virtual Machine Consolidation

A data center with a large number of servers, large storage, and many network devices requires power for cooling to reduce heat generation, air conditioning, and emergency power generation facilities, in addition to power for operation internally consumed by infrastructure equipment. The power consumed by data centers worldwide makes up a large proportion. Although the size of data centers is expected to increase, we are already faced with power problems because stability is prioritized over efficiency when operating data centers in order to meet the Service Level Agreement (SLA) conditions. Most data centers are in a virtualization environment, and virtual machine consolidation using physical machine (PM) transitions to the idle mode through virtual machine (VM) migration has been suggested as one of the most effective ways to reduce the amount of power usage in a data center. This study takes into account the characteristics of virtualization environments and presents an algorithm that effectively solves VM consolidation (VMC) through operator design using a grouping representation method and a meta-heuristic method known as harmony search.

Virtual machine consolidation: a systematic review of its overhead influencing factors

This survey is an up-to-date account of the research on virtual machine consolidation overhead. The overhead influencing factors are analyzed throughout this work. Based on these factors, we propose a categorization that classifies the most important research works on virtualization and virtual machine consolidation overhead. We have analyzed and summarized 46 selected research works from an initial set of 428, attempting to update the state of the art with the most recent papers in this field.

Burstiness-aware virtual machine placement in cloud computing systems

Virtual machine placement is one of the main sub-problems in the virtual machine consolidation process which faces different challenges. Burst-aware placement plays a key role in improving energy efficiency and reducing the SLA violations in cloud computing systems and hence requires special attention and investigation. Therefore, in this study, we will present burst-aware algorithms in order to decrease the resource wastage and reduce SLA violations. By presenting these algorithms, we aim to minimize the negative effects of workload bursts on the process of making decisions about the placement of virtual machines. We use random and real-world datasets and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding energy efficiency and performance, compared to the benchmark methods.

Virtual Machine Consolidation Method with Low Delay and Energy Consumption Parameters

Virtual Machine Consolidation Method with Low Delay and Energy Consumption Parameters

Bulk-bin-packing based migration management of reserved virtual machine requests for green cloud computing

The dynamic consolidation of Virtual Machines (VMs) into a minimum number of Physical Machines (PMs) is a key energy-efficient practice in a cloud data centre, to reduce the running PMs and save electricity costs. We proposed a migration based VM consolidation approach for reserved requests. Real Da

An intelligent ensemble learning approach for energy efficient and interference aware dynamic virtual machine consolidation

Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation algorithms can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing an energy and interference aware VM consolidation algorithm which uses predictive modelling to classify workloads using their resource usage features to make more informed consolidation decisions. The use of ensemble methods plays a pivotal role for improving predictive performance for many different problems. Using recent workload data from Microsoft Azure we present a comparative analysis of several ensemble methods using state-of-the-art prediction models and propose an ensemble based VM consolidation algorithm. Our empirical results demonstrate how our approach improves energy efficiency by 34% while also reducing service violations by 77%.

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model

For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.

Dynamic Resource Provisioning for Energy Efficient Cloud Radio Access Networks

Energy saving is critical for the cloud radio access networks (C-RANs), which are composed by massive radio access units (RAUs) and energy-intensive computing units (CUs) that host numerous virtual machines (VMs). We attempt to minimize the energy consumption of C-RANs, by leveraging the RAU sleep scheduling and VM consolidation strategies. We formulate the energy saving problem in C-RANs as a joint resource provisioning (JRP) problem of the RAUs and CUs. Since the active RAU selection is coupled with the VM consolidation, the JRP problem shares some similarities with a special bin-packing problem. In this problem, the number of items and the sizes of items are correlated and are both adjustable. No existing method can be used to solve this problem directly. Therefore, we propose an efficient low-complexity algorithm along with a context-aware strategy to dynamically select active RAUs and consolidate VMs to CUs. In this way, we can significantly reduce the energy consumption of C-RANs, while do not incur too much overhead due to VM migrations. Our proposed scheme is practical for a large-size network, and its effectiveness is demonstrated by the simulation results.

Performance Evaluation of Static VM Consolidation Algorithms for Cloud-based Data Centers with Predefined Machine Types

Performance Evaluation of Static VM Consolidation Algorithms for Cloud-based Data Centers with Predefined Machine Types

Measuring Inter-VM Performance Interference in IaaS Cloud

Virtualization has enabled cloud computing to deliver computing capabilities using limited computer hardware. Server virtualization provides capabilities to run multiple virtual machines (VMs) independently in a shared host leading to efficient utilization of server resources. Unfortunately, VMs experience interference from each other as a result of sharing common hardware. The performance interference arises from VMs having to compete for the hypervisor capacity and as a result of resource contention, which happens when resource demands exceed the allocated resources. From this viewpoint, any VM allocation policy needs to take into account VM performance interference before VM placement. Therefore, understanding how to measure performance interference is crucial. In this paper, we propose a simple experimental approach that can be used to measure performance interference in Infrastructure as a Service (IaaS) cloud during VM consolidation.

New Method for Load Balancingin Cloud Computing

Internet, since the beginning of its work, has undergone many changes one of the latest changes is how the internet cloud computing. New technologies cloud computing offers because of features all kinds of facilities to the users as a service. Each evolution, change and novel concept in the world of technologies has its own problems and complications. Accordingly, benefiting from cloud computing is no exception to this rule and it has challenged researchers and proponents in this research domain. Indeed, some major challenges in cloud computing are: load balancing, safety, reliability, ownership, data backup, data portability and supporting several platforms. One challenge for such matters in the field of cloud computing is load balancing optimization in the cloud. The so-called cloud computing, including virtualization, distributed computing, networking, software and Web services. With respect to the ever increasing significance of load balancing in cloud computing the researchers in this paper intended to improve load balancing by using a novel method. The related studies were reviewed, evaluated and compared with each other. The efficiency of the proposed method was analyzed and compared with those of other studies. The results of the present study revealed that the proposed method is better than other dynamic virtual machine (VM) consolidation algorithms in terms of reducing SLA (service levels agreement) violation and the amount of transmitted data volume transmission has to present a better performance than other methods.

Temperature and energy-aware consolidation algorithms in cloud computing

Cloud computing provides access to shared resources through Internet. It provides facilities such as broad access, scalability and cost savings for users. However, cloud data centers consume a significant amount of energy because of inefficient resources allocation. In this paper, a novel virtual machine consolidation technique is presented based on energy and temperature in order to improve QoS (Quality of Service). In this paper, two heuristic and meta-heuristic algorithms are provided called HET-VC (Heuristic Energy and Temperature aware based VM consolidation) and FET-VC (FireFly Energy and Temperature aware based VM Consolidation). Six parameters are investigated for the proposed algorithms: energy efficiency, number of migrations, SLA (Service Level Agreement) violation, ESV, time and space complexities. Using the CloudSim simulator, it is found that energy consumption can be alleviated 42% and 54% in HET-VC and FET-VC, respectively using our proposed methods. The number of VM migrations is reduced by 44% and 52% under HET-VC and FET-VC, respectively. The HET-VC and FET-VC can improve SLA violation by 62% and 64%, respectively. The Energy and SLA Violations (ESV) are improved by 61% under HET-VC and by 76% under FET-VC.

CPU–RAM-based energy-efficient resource allocation in clouds

Cloud computing provides online services and solutions for dynamic and on-demand resource provisioning. These resources consume high energy leading to higher operational expenditures and carbon footprints in data centers. There are several research works performed on energy efficiency of data centers, but mostly focus on energy consumption of a single factor, i.e., CPU, leaving the RAM neglected. Recently, the researchers have focused on the impact of RAM’s energy consumption on the data centers. Studies show that RAM consumes 25% of a server’s overall energy. In this paper, we propose two sets of schemes that consider the server capacity for virtual machine consolidation to reduce the overall energy cost. The proposed techniques are implemented in CloudSim, and the results are compared with state-of-the-art solutions. Our proposed techniques reduce energy consumption and maintain a service level agreement to satisfy the customer requirements with a minimum cost.

Energy-efficient and quality-aware VM consolidation method

To improve resource utilization and energy efficiency, cloud data centers use virtual machine (VM) consolidation to consolidate VMs to fewer physical machines (PMs) through live VM migration. However, improper VM placement may cause frequent VM migrations and constant on–off switching of PMs, which results in lower service quality and increased energy consumption. In this paper, we address this problem by proposing an effective and efficient VM consolidation approach called EQ-VMC, which has the goal of optimizing energy efficiency and service quality. In our approach, a discrete differential evolution algorithm is developed to search for the global optimum solution for VM placement. By integrating this solution with a set of algorithms proposed for effective host overload detection, VM selection, and under-loaded host detection, EQ-VMC effectively reduces energy consumption and improves quality of service (QoS). Extensive simulation demonstrates its effectiveness and shows its superiority to previous VM consolidation methods.

Multi-criteria-Based Energy-Efficient Framework for VM Placement in Cloud Data Centers

Energy efficiency has become a prime concern in this evolving era of cloud computing. In recent years, several VM consolidation techniques were proposed. However, they involve single-criterion‐based host selection methods, which are unsuitable for the dynamic cloud environment. Cloud data centers need an efficient utilization of physical host resources. The selected hosts for VMs deployment must ensure the least energy consumption and service ‐level agreement (SLA) violations simultaneously. There is a lack of multi-criteria‐based solution which considers multiple resources to select hosts for the deployment of VMs. This paper presents a VM deployment framework that considers multiple criteria, i.e., reducing energy consumptions and SLA violations simultaneously while determining target hosts for VMs placement. The article proposes an Improved Technique for Order of Preference by Similarity to Ideal Solution-based Host Selection Policy for VM deployment on the target hosts in terms of energy efficiency. It manages the trade-off between energy consumption and SLA compliance. A case study is demonstrated to prove the usefulness and appropriateness of the proposed framework. The results show that our framework is promising and provides more efficiency in the use of cloud resources and maintaining SLA.

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 regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud

Scheduling is a considerable problem in cloud to increase the quality of service provisioning with higher resource efficiency. The conventional task scheduling algorithms designed for balancing load in a cloud environment. But, minimizing the Service level agreement (SLA) violation, resource wastage and the energy consumption during the task scheduling process was not solved effectively. In order to resolve these limitations, a new virtual machine (VM) consolidation technique called Nature-inspired Meta-heuristic Threshold based firefly optimized lottery scheduling (NMT-FOLS) Technique is proposed. Initially, user requests are transmitted to the cloud server (CS). Next, NMT-FOLS Technique utilizes Adaptive Regressive Holt–Winters Workload Predictor to discover the workload state as normal or timely or bursty. Using the workload predictor result, NMT-FOLS Technique exploits task scheduler to allocate user requested tasks to optimal VMs. NMT-FOLS Technique applies multi-objective firefly optimization based task scheduling algorithm in normal workload state and multi-objective firefly optimized lottery scheduling algorithm in timely and bursty workload situations. At last, the selected scheduling algorithm in NMT-FOLS Technique assigns the user requested task to best VMs in CS to perform the demanded services. Hence, NMT-FOLS Technique gets better task scheduling performance to balance normal, timely and bursty workloads in CS with lesser time. NMT-FOLS Technique decreases the SLA violation in cloud through scheduling of user tasks to optimal VM. NMT-FOLS performs an experimental process using metrics such as SLA violation, task scheduling efficiency (TSE), makespan, energy utilization and memory usage with number of user-requested tasks over the considered Amazon dataset. From the experimental result, the NMT-FOLS technique improves scheduling efficiency up to 94.6% and reduces the SLA violations and energy utilization from different test cases on an average to 78%, and 63% compared to state-of-the-art works.

A survey on energy aware VM consolidation strategies

The rapid growth of Cloud computing is accompanied by a significant increase in the consumed energy by data centers. This huge increase in energy consumption has become a major concern because of both its costs and environment impact. Consolidating virtual machines (VMs) in minimum numbers of physical machines (PMs) is an effective way to reduce energy consumption. Various algorithms and techniques have been proposed to improve energy efficiency via VM consolidation. However, recent findings of Green Peace showed that energy demands of data centers are continuing to grow annually. This continuous increase queries the area of reducing energy consumption of data centers. As an attempt to figure out the reasons behind the gap between Green Peace recent findings on energy consumption and the proposed VM consolidation techniques and to bridge this gap, we undertake this survey. We discuss these techniques by highlighting their strengths and limitations and we justify the need for taking into consideration the energy overhead created by both the VM migrations and PMs transitions in and out of the low power mode.

Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters

Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.