Virtual Machine Consolidation Research Articles

Computing Frameworks for VM Migration in Cloud

Cloud Service users world wide are increasing with advancement in IT infrastructre. This leads to the demand of online storage for various operational reasons. The applications across various organizations are exploiting the cloud infrastructure offered by different service providers like Microsoft, Amazon, Twitter, IBM, and Hewlett-Packard, enable developers to distribute software through a central organization's hosting machine. Online storage is the next logical phase in the development of IT infrastructure. Cloud Storage Infrastructure has become a norm in distributed environment to host the multiple application on easily available networks. Rather than bothering about the Capital Expenditutre, the organizations are now using funds to invest in operational cost of running the services through Cloud service provider increasing the efficiency. While attempting to migrate the workload needs to be considered as imbalanced workload may result in system failure. Also the VM consolidation need to be done while the sytem is live without any failure. The security concerns for live migration are enlisted in limitations which is the largest concern for an organization whle deciding future course of action. This article reviews the cloud computing systems in the light of migrating the existing legacy systems to the cloud environment and suggest the operational features that an organization should consider before the migration.

An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification

AbstractCloud systems have become an essential part of our daily lives owing to various Internet‐based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy‐aware method for the VMC problem, which is called energy‐aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non‐functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.

Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.

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

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

Host utilization prediction using hybrid kernel based support vector regression in cloud data centers

The rapid growth in the cloud data center needs a dynamic resource provision to maintain the Quality of Services parameters. To guarantee it, Virtual Machine Migration as part of VM Consolidation has a significant role. Efficient VM migration requires knowledge of the host’s future utilization in advance. Because of the high variation in cloud resource usage and dynamic workloads, predicting host utilization using utilization history is challenging. This paper proposes a Support Vector Regression-based methodology to predict a host‘s future utilization using multiple resource's utilization history. A Hybrid Kernel function that includes radial basis function and polynomial kernel function has been proposed and then trains the Support Vector Machine using multiple-resource utilization history. Compared to the existing approaches: multiple linear regression-based prediction, Euclidean distance, and Absolute Summation based regression, the proposed method performs better in terms of root mean square error, mean square error, mean absolute percentage error, mean absolute error, and R2. The result section concludes that on evaluating error percent, the prediction error is 16% for the proposed approach and predicts host utilization with 7%, 64%, and 67% more accuracy than MRHOD, MDRHU-ED, MDRHU-AS approaches, respectively.

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

The rapid growth of cloud computing in the last decade has led to an increasing concern about the energy requirement of cloud data centers. Dynamic virtual machine (VM) consolidation is an effective way to tackle this issue, where VMs are executed on as few physical machines (PMs) as possible. Meanwhile, VM placement must be performed strategically, by considering different factors of the available resources to optimal exploitation of them. Moreover, a major challenge is the system’s reliability degradation because of the high frequency of consolidation and placing VMs on unreliable PMs. In this paper, we address the problem by introducing a discrete-time Markov chain (DTMC) model to predict future resource usage. Using the DTMC model along with the reliability model of PMs leads to more accurate PMs categorization based on their status. Then, a multi-objective VM placement approach is proposed to achieve the optimal VMs to PMs mapping using the ε-dominance-based multi-objective artificial bee colony (ε-MOABC) algorithm which can efficiently balance the overall energy consumption, resource wastage, and the system reliability to meet SLA and QoS requirements. We have validated the effectiveness of our proposed approach by conducting a performance evaluation study using the CloudSim toolkit. Competitive analysis of the experimental results demonstrates that the proposed approach significantly improves energy consumption while avoiding the inefficient VM migrations.

The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions.

A Combined Traffic and Workload-aware Optimized Virtual Machine Migration in Cloud Computing

In cloud computing paradigm, Virtual Machine (VM) migration strategies play a vital role in reducing the Energy Consumption (EC) and balancing the workloads in the cloud servers. From this viewpoint, an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction and Multipath Traffic Routing (OH-BAC-FUP-MTR) strategy was designed to accomplish effective load-balancing and minimize the chance of traffic congestion during VM migration in data centres. But, the tradeoffs among workload efficiency and power-gain in heterogeneous cloud servers were not analyzed. Hence in this article, a VM consolidation strategy is proposed with OH-BAC-FUP-MTR to switch the idle Physical Machines (PMs) into hibernation mode, resulting in very less EC. In this strategy, the VM migration is targeted at consolidating the VMs depending on the workload to the smaller number of PMs for reducing the power use and encouraging green computing. Initially, the PMs are split into various groups depending on their workload levels and then a new Merge-and-Split-based Coalitional Game-theoretic (MSCG) method is applied to select associates from these groups to create efficient coalitions. After that, OH-BAC-FUP-MTR is performed amongst the coalition associate for maximizing the reward of each coalition and PMs are maintained to operate in the maximum power-efficient condition. Finally, the investigational outcomes exhibit the OH-BAC-FUP-MTR-MSCG achieves a mean EC of 60.63KWh which is 28.87% less than all other classical VM migration strategies.

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

Virtual Machine Consolidation for Stochastic Load Balancing in Cloud Data Center Management

Cloud computing is able to managing a massive quantity of growing work for the use of enterprise clients in a specified way Virtualization, which makes assumptions the network resources and makes it simple to control, is an important enabling technology for cloud computing. Computing is being used in the proposed work to distribute cloud services tailored to the needs and to promote the smart grid principle. “Skewness” concept was delivered here wherein equal was reducing to combine workloads to enhance the usage of the server. The complexities of on-demand allocation of resources arise from managing customer demands. As a result, the use of vms technologies has proved to be helpful in terms of resource provisioning. The use of virtualized environments is expected to reduce primarily consist connection speed while also executing tasks in accordance with cloud resource availability. This implementation can be use local negotiation based VM consolidation mechanism to predict each job request and reduce overloads to create virtual space at the time of multiple requests. The proposed system implement co-location approach to combine unused small spaces to create new virtual space for improves the performance of server. Also implement self-destruction approach to eliminate the invalid data based on time to live property. The proposed framework is executed in genuine time with effective asset allotment. In this system to begin with broaden a forecast show which will gauge the parcel sizes of decrease commitments at runtime. And it can detect information skewness in real time and allocate extra asses for mordant of large walls that help us complete faster.

Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization

The scientific workflows are high-level complex applications that demand more computing power. The cloud data center (CDC) remains one of the essential models of economic infrastructure for workflow applications. These CDCs consume a lot of electric power while running workflow applications. Hence, efficient energy-aware scheduling techniques are required to perform the task to a virtual machine (VM) mapping. The existing researches overlooked to join the workflow scheduling and VM consolidation which addresses resource utilization and energy consumption effectively. In this article, we propose an energy-aware algorithm for workflow scheduling in cloud computing with VM consolidation called EASVMC. The proposed EASVMC approach is modeled to address the multi-objectives such as energy consumption, resource utilization, and VM migrations. The EASVMC algorithm runs in two phases task scheduling and VM consolidation (VMC). In the first phase, the task with maximum execution length is mapped to the virtual machine that will perform it with the minimum energy. The second phase contains VM consolidation is a prominent NP-hard problem. The VMC phase categorizes the physical hosts into the normal load, under-loaded and overloaded hosts based on CPU utilization. Double threshold values are used for this purpose. VMs from underloaded and overloaded hosts are migrated to normally loaded hosts. For the VMC phase, we used a nature-inspired meta-heuristic approach called the Water Wave Optimization (WWO) algorithm, which finds a suitable migration plan to reduce the energy consumption by increasing the overall resource utilization and switch off idle hosts after migrating its VMs to a suitable target host. The efficiency of our proposed method evaluated using the WorkflowSim simulation tool with five different real-world scientific workloads. The experimental results show that the EASVMC approach surpassed the similar works in stated objectives irrespective of diverse workloads.

A power and thermal-aware virtual machine management framework based on machine learning

Energy consumption in data centers grows rapidly in recent years. As a widely-applied energy-efficient method, workload consolidation also has its own limitations that may bring some negative effects, such as performance degradation, QoS violation, localized hotspots and so on, which is especially true when optimal objectives are inherently conflict. In this paper, we present a power and thermal-aware VM management framework called PTM-ML, which relies on machine learning technique to find optimal host configuration based on workload characteristics and cooling system’s working state. Based on such an optimal host configuration, it then makes VM migration and consolidation decisions by enforcing an efficient load-balancing policy, with aiming at achieving a better trade-off between energy efficiency and performance. The prototype of PTM-ML framework is deployed and evaluated in a real-world cloud data center. Extensive experiments are conducted by using different workload traces with distinctive characteristics, and the results are compared with four similar approaches in terms of total energy consumption, real-time power consumption, average latency and etc. Experimental results show that the proposed PTM-ML outperforms the existing approaches in terms of multiple metrics, and it also exhibits better robustness and adaptability in presence of dynamic workloads.

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

AbstractIn recent years, cloud computing has become more popular because of advancements in virtualization technology. By increasing the number of servers in cloud computing environment, cloud data centers have expanded and consumed much energy. Virtual machine consolidation is a solution for energy management in cloud environment. On the other hand, by increasing resource utilization in virtual machine consolidation, service level agreement assurance is difficult to obtain. Two main challenges in virtual machine consolidation are timely detection of overloaded servers and proper immigrant virtual machine selection from detected servers. In this paper, a new model is proposed based on MAPE‐k loop for autonomous virtual machine selection. The presented model uses a proposed ensemble prediction algorithm in the analysis phase. Also, in the planning phase, a new multi‐heuristics algorithm with flexible weights using learning automata is proposed. The effectiveness of the proposed model is evaluated by CloudSim simulator under real workload as compared with well‐known algorithms in this domain. The experimental results indicate that, the proposed approach has averagely improved the balance between service level agreement violations, energy and migration counts by 47.39% compared to other methods.

Interval-valued Fuzzy Logic approach for overloaded hosts in consolidation of virtual machines in cloud computing

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing. For example, determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system. This paper presents a new proposal with an Interval-valued Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. The results obtained in a simulated cloud computing environment showed improvements for energy efficiency of 2.3% with Inter Quartile Range (IQR). With regard to Service Level Agreement (SLA), results of 10.1% were achieved with Threshold Policy (THR), and finally, IQR obtained 7.9% better results considering the reduction in the number of VM migrations.

Energy efficient virtual machine migration approach with SLA conservation in cloud computing

In the age of online workload explosion, cloud users are increasing exponentialy. Therefore, large scale data centers are required in cloud environment that leads to high energy consumption. Hence, optimal resource utilization is essential to improve energy efficiency of cloud data center. Although, most of the existing literature focuses on virtual machine (VM) consolidation for increasing energy efficiency at the cost of service level agreement degradation. In order to improve the existing approaches, load aware three-gear THReshold (LATHR) as well as modified best fit decreasing (MBFD) algorithm is proposed for minimizing total energy consumption while improving the quality of service in terms of SLA. It offers promising results under dynamic workload and variable number of VMs (1–290) allocated on individual host. The outcomes of the proposed work are measured in terms of SLA, energy consumption, instruction energy ratio (IER) and the number of migrations against the varied numbers of VMs. From experimental results it has been concluded that the proposed technique reduced the SLA violations (55%, 26% and 39%) and energy consumption (17%, 12% and 6%) as compared to median absolute deviation (MAD), inter quartile range (IQR) and double threshold (THR) overload detection policies, respectively.

An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing

With the increase of mega-cities, the demand for Smart Cities is overgrowing. The mega-cities can be smarter through the Cloud of Things (CoT). Efficient energy consumption in Smart Cities has a massive impact on the environment. But, computational power is increasing rapidly in the cloud computing environment. Enormous energy consumption (EC) and Service Level Agreement violation (SLAV) becomes a key concern. The Virtual Machine (VM) consolidation approach can significantly reduce EC, SLA violation (SLAV), and increase resource utilization. However, dynamic VM consolidation may produce performance degradation of Physical Machines (PMs) and SLAV. Therefore, it becomes essential to find a trade-off between EC and SLAV. Herein, a novel New Linear Regression(NLR) prediction model, host overload/underload, and VM placement policy have been proposed to reduce EC and SLAV. The NLR model’s primary intention is to take that the model goes through a straight line and a mean point. Future CPU utilization is predicted based on the proposed NLR model. Evaluation of proposed algorithms has been accomplished by extending CloudSim Simulator. The experiment shows that proposed algorithms reduced EC and SLAV in cloud data centers and can be used to construct a smart and sustainable environment for Smart Cities.

Application of virtual machine consolidation in cloud computing systems

Cloud systems play a vital and significant role in our daily lives due to various internet services. For instance, email services, social networks, and others. Consequently, their energy consumption has also become an increasingly essential concern in Cloud Computing Systems (CCSs). Virtualization technologies are widely used to facilitate the management of CCSs and reduce their energy consumption. Virtualization enables live migration of Virtual Machines (VMs) where several VMs can be loaded on some Physical machines (PMs) called VM consolidation. A VM consolidation algorithm can be an effective technique for reducing energy consumption, operational cost, hardware cost, Service Level Agreements (SLAs) compliance/violation, CO2 emissions, and enhancing the hardware and service reliability, performance, and hardware lifetime, load balancing, and utilization in CCSs. Essentially, VM consolidation must minimize energy utilization and service quality in the cloud system. This study presents a taxonomy comprising resource assignment method, metrics, objective functions, migration methods, algorithmic methods, co-location criteria of VMs, architectures, workload dataset, and evaluation approaches in VM consolidation CCSs. Also, we reviewed related work regarding the resources of PMs, algorithm methods, metrics, architectures, and the objectives in static/dynamic VM consolidation.

Managing overloaded hosts for energy-efficiency in cloud data centers

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively.

Management of cloud resources and social change in a multi-tier environment: A novel finite automata using ant colony optimization with spanning tree

The enormous demand for computational resources due to rapid Cloud growth has led to the creation of large-scale data centers (DC). Consequently, a tremendous amount of energy is consumed with high carbon dioxide emissions, which necessitates that cloud service providers (CSP) develop high quality of service (QoS) strategies to address such challenges. Proper management and utilization of resources ensure a well-balanced load distribution that makes energy consumption sufficient. Developing new algorithms and exploring efficient methods and techniques is highly desired for the management of virtualized DCs. This research proposes Finite Automata using Ant Colony Optimization with Spanning Tree (FAACOST), a machine-learning concept for managing cloud resources in a multi-tier environment. The overall objective is to minimize energy consumption through data placement leverage and virtual machines (VM) consolidation. The proposed technique's efficiency was benchmarked on four performance metrics (machine learning, dynamic nature, scalability, and QoS). Based on the extensive experiments conducted, FAACOST recorded an energy consumption of 132 w with 20 tasks compared to the benchmarked techniques that consumed 363 w. The experimental results show that FAACOST achieves the optimal number of physical machines (PMs) and is more energy-efficient.

Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers

Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Virtual Machine (VM) consolidation is one technique that can be harnessed to reduce energy related costs and environmental sustainability issues of data centers. In recent times intelligent learning approaches have proven to be effective for managing resources in cloud data centers. In this paper we explore the application of Reinforcement Learning (RL) algorithms for the VM consolidation problem demonstrating their capacity to optimize the distribution of virtual machines across the data center for improved resource management. Determining efficient policies in dynamic environments can be a difficult task, however, the proposed RL approach learns optimal behavior in the absence of complete knowledge due to its innate ability to reason under uncertainty. Using real workload data we provide a comparative analysis of popular RL algorithms including SARSA and Q-learning. Our empirical results demonstrate how our approach improves energy efficiency by 25% while also reducing service violations by 63% over the popular Power-Aware heuristic algorithm.