Virtual Machine Consolidation Research Articles

Self-adaptive resource allocation for energy-aware virtual machine placement in dynamic computing cloud

Cloud computing is a type of Internet-based computing that provides shared computing resources and data on demand. To support the massive cloud services, a data center is built with thousands of servers and switches connected by a communication network called a data center network (DCN). Along with the development of virtualization, the data center manager conducts virtual machine (VM) resizing and live migrations to adjust the allocation of VMs and improve the server utilization. However, compared to VM resizing, live migrations can negatively impact the performance of the system depending on the amount of transferred data and the path length. In this paper, we propose an online resource allocation algorithm using VM consolidation to achieve energy efficiency and reduce service-level agreement (SLA) violations of data centers while considering the power usage of servers, the number of migrations, and the path length of migrations in DCNs. Compared to the state-of-the-art mechanism, the proposed algorithm successfully reduces the power consumption, the number of migrations, and the path length of migrations for a dynamic cloud service.

Energy efficient dynamic resource management in cloud computing based on logistic regression model and median absolute deviation

The unprecedented trend of using public cloud computing services by increasing number of customers motivates cloud services providers to optimize their resources usage and management to the limit. This is including managing cloud user’s virtual machines (VM) running on one or more of the thousands of hosting servers or physical machines (PMs) of the cloud datacenters. The cloud service providers are mainly concerned on answering the two main questions that dramatically impact their infrastructure usage and utilization; Where to initially place the VMs and where to move them in case we need to move them. Along with the VM consolidation technique, VMs migration will help in protecting the physical servers from being overloaded or reduce the number of active physical servers for better resources utilization and energy saving. Efficiently detecting overloaded servers will help in improving the cloud system performance and reduce the total operational costs which will provide competitiveness for the cloud provider in the market. In this work, we are proposing a general host overloading detection algorithm based on logistic regression model and median absolute derivation. The proposed algorithm is scalable and can be used with any VM placement and migration algorithms. An extensive evaluation procedure is used with dynamic workload to proof the efficiency of the proposed algorithm. The archived results show that the proposed algorithm outperforms all other known host status prediction techniques.

Investigation of Security Breaches Due to Resource Sharing in Virtual Machine Migration Using Hybrid Ant Colony Optimization with ANN

Cloud computing is one of the active research areas in High Performance Computing. It helps to share the resources globally in a distributed manner. In this paper, hybrid ACO with ANN is designed to ensure the best and the secured VM consolidation process. Initially, the objectives constraints for

Dynamic VM Consolidation Enhancement for Designing and Evaluation of Energy Efficiency in Green Data Centers Using Regression Analysis

Enhancement of dynamic Virtual Machines (VM) consolidation is an efficient means to improve the energy efficiency via effective resources utilization in Cloud data centers. In this paper, we propose an algorithm, Energy Conscious Greeny Cloud Dynamic Algorithm, which considers multiple factors such as CPU, memory and bandwidth utilization of the node for empowering VM consolidation by using regression analysis model. This algorithm is the combination of several adaptive algorithms such as EnCoReAn (UPReAn) for Predicting the Utility of a host), Overload and Under-load detection), VM Selection and Allocation algorithms, which helps to achieve live VM migration by switching-off unused servers to low-power mode (i.e., sleep or hibernation), thus saves energy and efficient resource utilization. This approach reduces the operational cost, computation time and increase the scalability. The experimental result proves that, the proposed algorithm attains significant percentage in reduction of energy consumption rather than existing VM consolidation strategies.

DADTA: A novel adaptive strategy for energy and performance efficient virtual machine consolidation

Large-scale virtualized data centers are increasingly becoming the norm in our data-intensive society. One pressing challenge is to reduce energy consumption in such data centers for edge computing deployment, which would have flow-on effects on reducing the operating costs and carbon dioxide emissions. Dynamic virtual machine consolidation is an effective way to improve resource utilization and energy efficiency. In this paper, a comprehensive strategy is proposed, which is based on time-series forecasting approach. In this strategy, specific adjustment of threshold is applied to adapt the dynamic workload. We then use a real-world dataset (i.e. workload trace in Google) for evaluation, whose findings demonstrate that our strategy outperforms other benchmarks.

On the Carbon Footprint Optimization in an InterCloud Environment

In this paper, we address the problem of Virtual Machine (VM) placement in an InterCloud with regard to the reduction of carbon footprint in such computing environment. In order to minimize data centers’ Greenhouse Gas (GhG) emissions, this paper proposes a mathematical formulation, where the placement approach is stated as a Mixed-Integer Nonlinear Programming (MINLP) problem which aims at minimizing the carbon footprint of the InterCloud. The proposed formulation presents an accurate carbon footprint evaluation based on joint optimization techniques, such as smart and performance-aware workload consolidation, along with cooling efficiency maximization, while considering the greenness factor of the data centers and the dynamic behavior of the equipment cooling fans. Simulations with an exact method showed that, compared with other baseline approaches, the proposed mathematical model leads to optimal configurations with minimal GhG emissions in a single cloud, as well as in an InterCloud environment, and can yield savings of up to 65 percent. The proposed model has also been compared with an approach that performs blind VM consolidation, and the obtained results showed that such model can simultaneously lead to VM configuration that yields the minimum carbon footprint while performing smart VM consolidation in order to prevent Service Level Agreement (SLA) violations.

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.

Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.

Dynamic Workload Migration Over Backbone Network to Minimize Data Center Electricity Cost

As more organizations adopt cloud services, energy consumption in data centers (DCs) keeps increasing. Today, information and communication technology (ICT) has become a major consumer of energy worldwide. A large portion of ICT energy consumption is used to power servers running in DCs and the network they use to communicate. In this paper, we consider that energy cost at a particular DC is often related to the electricity price regulated by independent system operators/regional transmission organizations. As these prices vary in time and depend on the geographical locations of the DCs, recent studies have shown that the spatio-temporal variations of electricity price can be exploited to reduce electricity cost. In particular, as workloads tend to change, often unpredictably, adaptive workload placement and migration can help to serve the workloads in regions with lower electricity costs. While most prior works consider a quasi-static scenario with known workload patterns, this paper proposes dynamic workload-aware algorithms which exploit the spatio-temporal variations of electricity costs to minimize the energy cost in ICT. Although prior studies focused on power consumption from power consumers such as servers, cooling systems, etc., recent studies have shown that the network elements consume a significant portion of the energy. Hence, while reducing DC energy cost, this paper also considers electricity cost of the backbone network. Algorithms introduced in this paper use dynamic request re-routing and live virtual machine (VM) migration to move workloads to DCs with lower electricity cost. We consider VM migration cost (including electricity cost at backbone network nodes), bandwidth constraints for migration, VM consolidation, constraints from service level agreement, and administrative overhead of VM migration. Our simulation studies show that the proposed algorithms reduce operational cost of DCs significantly.

CompVM: A Complementary VM Allocation Mechanism for Cloud Systems

In cloud datacenters, effective resource provisioning is needed to maximize the energy efficiency and utilization of cloud resources while guaranteeing the service-level agreement (SLA) for tenants. To address this need, we propose an initial virtual machine (VM) allocation mechanism (called CompVM) that consolidates complementary VMs with spatial/ temporal awareness. Complementary VMs are the VMs whose total demand of each resource dimension (in the spatial space) nearly reaches their host’s capacity during VM lifetime period (in the temporal space). Based on our observation of the existence of VM resource utilization patterns, the mechanism predicts the resource utilization patterns of VMs. Based on the predicted patterns, it coordinates the requirements of different resources and consolidates complementary VMs in the same physical machine (PM). This mechanism reduces the number of PMs needed to provide VM service, hence increases energy efficiency and resource utilization, and also reduces the number of VM migrations and SLA violations. We further propose a utilization variation-based mechanism, a correlation coefficient-based mechanism, and a VM group-based mechanism to match the complementary VMs in order to enhance the VM consolidation performance. Simulation based on two real traces and real-world testbed experiments shows that CompVM significantly reduces the number of PMs used, SLA violations, and VM migrations of the previous resource provisioning strategies. The results also show the effectiveness of the enhancement mechanisms in improving the performance of the basic CompVM.

ALGORITHM FOR TASK CONSOLIDATION IN CLOUD COMPUTING: A COMPARATIVE SURVEY

Cloud Computing is an emerging field in the IT industry. Cloud computing provides computing services over the Internet. Cloud Computing demand increasing drastically, which has enforced cloud service provider to ensure proper resource utilization with less cost and less energy consumption. In recent time various consolidation problems found in cloud computing like the task, VM, and server consolidation. These consolidation problems become challenging for resource utilization in cloud computing. We found in the literature review that there is a high level of coupling in resource utilization, cost, and energy consumption. The main challenge for cloud service provider is to maximize the resource utilization, reduce the cost and minimize the energy consumption. The dynamic task consolidation of virtual machines can be a way to solve the problem. This paper presents the comparative study of various task consolidation algorithms.

Type-aware virtual machine management for energy efficient cloud data centers

To meet the growing demands on cloud services and applications, a sizeable number of large scale cloud data centers, hosting thousands of heterogeneous servers, is established by cloud service providers. The ever growth in establishing cloud data centers is accompanied by consuming enormous amounts of energy. Thus, proposing efficient management approaches to reduce the energy consumption in cloud data centers becomes a top priority for ensuring the scalability of the cloud computing architecture. In general, the main source of energy overconsumption in today’s data centers is due to the inefficient use of the physical servers’ resources, which results in poor server utilization patterns. So, the key aspect is to utilize the physical resources optimally while serving the cloud user demands. This paper investigates the design and implementation of virtual machine management strategies for energy efficient cloud data centers. Particularly, it considers the processes of virtual machine placement and virtual machine consolidation in enhancing the energy efficiency in cloud infrastructures. While addressing the virtual machine placement problem is important, virtual machine consolidation is even more important to enable continuous reorganization of the already-placed virtual machines on the least number of physical machines. This results in reducing the number of active physical machines by leveraging live virtual machine migration enabled by the virtualization concept. Moreover, since the virtual machine migration operations consume additional energy, the frequency of VM migrations needs to be limited and controlled as well. The paper presents a distributed approach to an energy-efficient dynamic virtual machine consolidation mechanism. This approach determines, based on novel algorithms, which virtual machines to migrate, and when. Then, the placement of the virtual machines selected for migration is achieved based on a generalization of the Knapsack Problem known as the Multiple Choice Knapsack Problem. The placement process suits both static and dynamic virtual machine placement. The results of the performance evaluation demonstrate that the proposed new algorithms are able to enhance the energy efficiency in cloud data centers.

Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management

Cloud resource management becomes more important with the increasing usage of cloud resources. With various cloud options available, cloud provider may have different priority in managing the resource through resource scheduling and provisioning. Dynamic VM (Virtual Machine) consolidation algorithm is one of the techniques which can be used to reduce energy consumption through VM migration. Higher VM migration may lead to lower energy consumption and higher SLA violation. Although previous research has successfully decreased energy consumption and SLA violation, cloud providers may need to manage trade-offs between energy and SLA violation through availability of priority in the system. This paper proposes neural network-based adaptive selection of VM consolidation algorithms which adaptively chooses appropriate algorithm according to cloud provider’s goal priority and environment parameters. Dataset generation and performance evaluation using simulations on real-world PlanetLab VMs workload trace showed that adaptive selector produced better average performance score than independent methods on various evaluation priority.

Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

Energy and cost‐aware virtual machine consolidation in cloud computing

SummaryCloud computing has become an essential part of the computational world, offering a variety of server capabilities as scalable virtualized services. Big data centers that deliver cloud computing services contain thousands of computational nodes that consume a significant amount of energy. By introducing the virtual machine (VM), virtualization technology is trying to overcome this problem. One impressive technique for minimizing the total number of active physical servers that lead to improved energy consumption is VM consolidation. To optimize the consolidation process, effective VM placement can be used. In this paper, we first present a mathematical model aimed at reducing power consumption and costs by employing an effective VM consolidation in the cloud data center. Subsequently, we propose a genetic algorithm–based meta‐heuristic algorithm, namely, energy and cost‐aware VM consolidation for resolving the problem. Finally, we compare our proposed model with the well‐known first fit, first fit decreasing, and permutation pack algorithms. The experimental results show that our proposed model reduced power consumption and costs when compared with the three demonstrated algorithms.

Enhancing Constraint-Based Multi-Objective Combinatorial Optimization

Minimal Correction Subsets (MCSs) have been successfully applied to find approximate solutions to several real-world single-objective optimization problems. However, only recently have MCSs been used to solve Multi-Objective Combinatorial Optimization (MOCO) problems. In particular, it has been shown that all optimal solutions of MOCO problems with linear objective functions can be found by an MCS enumeration procedure. In this paper, we show that the approach of MCS enumeration can also be applied to MOCO problems where objective functions are divisions of linear expressions. Hence, it is not necessary to use a linear approximation of these objective functions. Additionally, we also propose the integration of diversification techniques on the MCS enumeration process in order to find better approximations of the Pareto front of MOCO problems. Finally, experimental results on the Virtual Machine Consolidation (VMC) problem show the effectiveness of the proposed techniques.

Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing

Cloud computing offers utility-based IT services on-demand to the users on a pay-per-use-basis. The cloud centers consist of physical machines (PMs) with virtual machines (VMs). These data centers consume a large amount of energy due to the improper resource utilization and lack of efficient schedu ling algorithms to perform the task-resource mapping. These issues lead to huge energy consumption along with high maintenance costs and carbon emissions. In this paper, a Power Efficient Scheduling and VM Consolidation (PESVMC) algorithm is proposed to address these issues and the associated challenges. The numerous existing research works concentrated on the application of energy management techniques to hardware level support for the reduction of energy consumption. The proposed algorithm emphasizes on the software level by taking the flexibility of the virtualization technology and it consists of two phases, VM Scheduling phase, and VM Consolidation phase. In the Scheduling phase, the tasks with maximum runtime are allocated to VM, which is expected to consume minimal energy. In the VM Consolidation phase, overloaded and underloaded hosts are determined based on the double-threshold scheme. Further, Live Migration technique is applied for migrating the VMs from over-utilized or underutilized hosts to other hosts with the normal utilization. A power efficient utilization factor is introduced to determine the underloaded hosts. This utilization factor is proven to reduce the number of migrations, which can cause additional energy consumption. Energy efficient Scheduling combined with VM Consolidation is successful in maximizing the resource utilization and minimizing the energy consumption. The experimental evaluation is performed using WorkflowSim and the proposed algorithm achieves significant energy conservation and resource utilization.

Data analytics for energy-efficient clouds: design, implementation and evaluation

ABSTRACTThe success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. Predictive data mining models can be exploited for this purpose. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving.