Live Migration Of Virtual Machines Research Articles

QoS Guaranteed Resource Allocation for Live Virtual Machine Migration in Edge Clouds

Live Virtual Machine (VM) migration among geographically distributed edge clouds is an important strategy for providing low latency and reliable services for mobile end users. VM migration among edge clouds is more challenging than that in cloud computing, because the network bandwidth among edge clouds is more constrained than the cloud data center networks. In this paper, we study the bandwidth allocation among multiple concurrent live VM migrations in edge clouds. This problem is novel in that existing works aim to reduce the migration time for a single live VM migration among the edge clouds, and also ignores the QoS requirement for the service running on the VM in migration. However, our problem considers multiple VM migration tasks, and aims to maximize the average QoS while meeting the migration time constraint for each VM migration task. We formulate the problem as a Non-Linear Programming (NLP) problem which is also shown to be NP-Hard. We develop a new method to solve this problem. In our approach, we first transfer the problem into a Linear Programming (LP) problem by reducing the solution space. Taking the output from the LP solver as an initial solution, we then develop a heuristic to adjust it in order to find a better one to the original NLP problem. Finally, we design a set of evolutionary algorithms to select the optimal initial solution from the LP solver. Extensive simulations show that our proposed method can achieve good QoS and also has a fast convergence speed.

Analyzing Software Rejuvenation Techniques in a Virtualized System: Service Provider and User Views

Virtualization technology has promoted the fast development and deployment of cloud computing, and is now becoming an enabler of Internet of Everything. Virtual machine monitor (VMM), playing a critical role in a virtualized system, is software and hence it suffers from software aging after a long continuous running as well as software crashes due to elusive faults. Software rejuvenation techniques can be adopted to reduce the impact of software aging. Although there existed analytical model-based approaches for evaluating software rejuvenation techniques, none analyzed both application service (AS) availability and job completion time in a virtualized system with live virtual machine (VM) migration. This paper aims to quantitatively analyze software rejuvenation techniques from service provider and user views in a virtualized system deploying VMM reboot and live VM migration techniques for rejuvenation, under the condition that all the aging time, failure time, VMM fixing time and live VM migration time follow general distributions. We construct an analytical model by using a semi-Markov process (SMP) and derive formulas for calculating AS availability and job completion time. By analytical experiments, we can obtain the optimal migration trigger intervals for achieving the approximate maximum AS availability and the approximate minimum job completion time, and then service providers can make decisions for maximizing the benefits of service providers and users by adjusting parameter values.

Energy-efficient and Dynamic Consolidation of Virtual Machines in OpenStack-based Private Cloud

Abstract Dynamic Virtual Machines (VMs) consolidation is an efficient technique for enhancing resource utilization and reducing energy consumption by the physical servers. Over-utilized host affects the degradation of resource utilization and VM performance. Determining when it is best to migrate VMs from an over-utilized host to an under-utilized host is the aim of dynamic Live VM Migration (LVM). It influences the resource utilization and Quality of Services (QoS) offered by cloud providers. In the OpenStack cloud, LVM is done manually, and it uses first-fit as a default migration technique, which is not efficient for resource utilization. The paper proposes a novel approach for the dynamic consolidation of VMs in the OpenStack cloud. The CPU utilization, RAM utilization, and the number of instances of each host are monitored over a period of time, and overload detection of a host is carried out using the SVM classification model. The Load balancing consolidation is performed based on the SVM classification result. Further, an energy-efficient consolidation achieved to optimize the energy of physical servers. We carry out the performance analysis of the proposed work in the OpenStack-based real-time testbed of five-node. The result reveals the fair distribution of load within the servers and the significant benefits of energy-efficient VM consolidation resulting up to 15% energy savings.

Performance-to-Power Ratio Aware Resource Consolidation Framework Based on Reinforcement Learning in Cloud Data Centers

Dynamic consolidation of virtual machines (VMs) is presented as a significant technique of energy conservation in cloud environments, which can eliminate the hotspot of overloaded hosts and switch the under loaded hosts to sleep mode through live migration of virtual machines. However, since the fact that migrating VM consumes a certain amount of extra resources, the process of reallocation can cause Service Level Agreement (SLA) violations. In this paper, a novel proactive framework which considers both predicted resource utilization and Performance-to-power Ratio (PPR) of heterogeneous hosts is proposed to perform dynamic VM consolidation to achieve balance of performance and energy. More precisely, a workload predictor is proposed based on the modified Weighted Moving Average (WMA) algorithm, representing the support for dynamic resource allocation; a cluster controller is proposed based on reinforcement learning for exploring the optimal matching relationship between resource requests and host at different PPR levels; a resource allocator is designed based on greedy strategy for achieving the trade-off between energy consumption and application performance across the cluster. Moreover, the framework is implemented based on distributed architecture and off-line learning pattern, which are able to not only scale up quickly but also improve the computing efficiency of the system. To validate the effectiveness of the proposed method, we have performed experimental evaluation on CloudSim with real-world workload traces of PlanetLab, and the simulation results demonstrate that it reduces the energy consumption up to 45.25% and effectively deals with high Quality of Service (QoS) requirements and heterogeneous distributed infrastructures in comparison with other competitive approaches.

Dynamic Hybrid-copy Live Virtual Machine Migration: Analysis and Comparison

Abstract Live VM migration has significantly gained attention as one of the most key strategies of improving data center efficiency through better resources allocation that leads to efficient services. The data center efficiency includes less power consumption, less unutilized resources, and less service interruption time in cases of maintenance or failures. The key point of the live VM migration is to copy the VM’s state, memory, and disk with the minimal downtime to guarantee less interruption in the services the VM provides. Pre-copy, Post-copy, and Hybrid-copy are examples of Live VM migration methods that copy the VM’s memory states to the destination host, while the VM still runs at the source host, as in the Pre-copy, or at the destination host, as in Post-copy. Each of these classical live migration methods has different characteristics and performance. Dynamic Hybrid-copy combines Pre-copy iterations with the Post-copy to have the best of both methods. In this paper, we present the mathematical model for the Dynamic Hybrid-copy live migration method for the total migration time, downtime, and total transferred data during the migration process. We compare the Dynamic Hybrid-copy with other classical live migration methods. We implement the models on JAVA and evaluate the performance of the Dynamic Hybrid-copy and compare it with other methods for different experimental parameters. Based on the performance results, we show how choosing the effective number of Pre-copy iterations leads to better migration performance. Mathematical models were also implemented in CloudSim and a simulation scenario was tested for the four migration methods.

Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the live VM migration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated. Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previous VM placement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs.

A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

The platform for cloud computing offers virtualization and a dynamic pool of resources to the consumers of the cloud. The acceptance and demand of cloud is growing on a regular basis. Cloud computing offers utility-based consumer services across the globe on a pay as you go strategy. Live Virtual Machine (VM) migration sets the basic foundation for cloud management. It hasakey role in reducing operating expenditure and revising quality of service without disruption of cloud services running in the VM. A lot of research has been done to yield better performance in live VM migration and has seen noteworthy development and accomplishment. However, some crucial problems require results and enhancement. With the growth of new cloud computing models like Mobile Edge Computing, certain problems related to optimization need to be addressed. The primary aim of this research work is to emphasize on optimum functioning of live migration. A migration algorithm to consolidate the computational resources, storage resources and network resources dynamically with a two-stage heuristic hybrid evolutionary algorithm is discussed. The resources are consolidated to cut down the energy and cost utilization depending upon the evolutionary Particle Swarm Optimization and the Ant Colony Optimization algorithms. These algorithms can rapidly identify the migrating virtual machines and locate their positions respectively.

An adaptive resource placement policy by optimizing live VM migration for ITS applications in vehicular cloud network

AbstractVehicular cloud computing (VCC) is a promising approach that uses cloud computing techniques in vehicular environment to execute smart applications. Vehicular environment is characterized by high mobility. This requires the services executed by a vehicular resource in a vehicular cloud to be migrated before it leaves a network. Migration is also required when a service‐requesting vehicle has to leave a network before its request is executed. Moreover, to balance the network load, virtual machine (VM) migration is desired as, at times, a single physical host may become overloaded. In this work, we propose an adaptive resource placement policy by optimizing live VM migration process in vehicular cloud network. The Pareto optimal mapping of migrating VMs to a physical host is carried out using a hybrid optimized algorithm, which is the combination of particle swarm optimization and genetic algorithm. The optimization is done by maximizing a fitness function that is developed by considering significant quality‐of‐service (QoS) parameters, such as network latency, migration delay, power consumption, and vehicular mobility. These QoS parameters are mathematically formulated in view of a vehicular network. The proposed algorithm reduces migration latency, transmission latency, and service delay. It also distributes the load among the available physical hosts within a cloud system, thus reducing the average wait time for each cloud user. Simulation results exhibit significant decrease in waiting time, service delay, and migration delay. It is also shown that the proposed algorithm reduces total power consumption of the system.

On demand clock synchronization for live VM migration in distributed cloud data centers

Live migration of virtual machines (VMs) has become an extremely powerful tool for cloud data center management and provides significant benefits of seamless VM mobility among physical hosts within a data center or across multiple data centers without interrupting the running service. However, with all the enhanced techniques that ensure a smooth and flexible migration, the down-time of any VM during a live migration could still be in a range of few milliseconds to seconds. But many time-sensitive applications and services cannot afford this extended down-time, and their clocks must be perfectly synchronized to ensure no loss of events or information. In such a virtualized environment, clock synchronization with minute precision and error boundedness are one of the most complex and tedious tasks for system performance. In this paper, we propose enhanced DTP and wireless PTP based clock synchronization algorithms to achieve high precision at intra and inter-cloud data center networks. We thoroughly analyze the performance of the proposed algorithms using different clock measurements. Through simulation and real-time experiments, we also show the effect of various performance parameters on the data center networking architectures.

Performance degradation assessment and VM placement policy in cloud

In virtualized servers, with live migration technique pages are copied from one physical machine to another while the virtual machine (VM) is running. The dynamic migration of virtual machines encumbers the data center which in turn reduces the performance of applications running on that particular physical machine. A considerable number of studies have been carried out in the area of performance evaluation during live VM migration. However, all the aspects related to the migration process have not been examined for the performance assessment. In this paper, we propose a novel approach to evaluate the performance during migration process in different types of coupled machine environment. It is presented here that the state of art VM migration technology requires further improvement in realizing effective migration by monitoring comprehensive performance value. We introduced the parameter, θ, to compare performance value which can be used for controlling and halting unsuccessful migration and save significant amount of time in migration operation. Our model is capable of analyzing real time scenario of cloud performance assessment targeting VM migration strategies. It also offers the possibility of further expanding to universal models for analyzing the performance variations that occurs as a result of VM migration.

Analytical evaluation of an innovative decision-making algorithm for VM live migration

In order to achieve the virtual machines live migration, the two pre-copy and post-copy strategies are presented. Each of these strategies, depending on the operating conditions of the machine, may perform better than the other. In this article, a new algorithm is presented that automatically decides how the virtual machine live migration takes place. In this approach, the virtual machine memory is considered as an informational object that has a revision number and it is constantly changing. We have determined precise criteria for evaluating the behavior of a virtual machine and automatically select the appropriate live migration strategy. Also in this article, different aspects of required simulations and implementations are considered. Analytical evaluation shows that using the proposed scheme and the presented algorithm, can significantly improve the virtual machines live migration process.

An overview of virtual machine live migration techniques

In a cloud computing the live migration of virtual machines shows a process of moving a running virtual machine from source physical machine to the destination, considering the CPU, memory, network, and storage states. Various performance metrics are tackled such as, downtime, total migration time, performance degradation, and amount of migrated data, which are affected when a virtual machine is migrated. This paper presents an overview and understanding of virtual machine live migration techniques, of the different works in literature that consider this issue, which might impact the work of professionals and researchers to further explore the challenges and provide optimal solutions.

An Adaptive Intrusion Detection Scheme for Cloud Computing

To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.

A Strategy for Live Migration of Virtual Machines in a Cloud Federation

Migration of virtual machines (VMs) from one data center to another is essential to deal with scenarios such as load balancing, maintenance, power management, VM failures, etc. Application of VM migration is not only limited to a single cloud service provider, but also to inter-cloud architectures such as cloud federation. Migration in a federation is a complex operation as it encompasses many autonomous service providers with different standards, rules, and protocols. Migration can be carried out in two ways, first, live and, second, non-live. In case of live migration, a VM continues to execute at the source in contrast to non-live where its execution is suspended. In this paper, a framework for secure live migration of VMs in a cloud federation is proposed. Costs associated with it is analyzed for serial, parallel, and improved serial strategies. The proposed framework is simulated using a CloudSim simulator. Metrics considered for evaluation are communication overhead, migration time, and downtime. Further, power consumption for all strategies is calculated and compared. It is observed that downtime is least for parallel, whereas migration time is least for improved serial. The power consumption is highest for parallel and least for improved serial.

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.

Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines

Cloud providers leverage live migration of virtual machines to reduce energy consumption and allocate resources efficiently in data centers. Each migration decision depends on three questions: when to move a virtual machine, which virtual machine to move and where to move it? Dynamic, uncertain, and heterogeneous workloads running on virtual machines make such decisions difficult. Knowledge-based and heuristics-based algorithms are commonly used to tackle this problem. Knowledge-based algorithms, such as MaxWeight scheduling algorithms, are dependent on the specifics and the dynamics of the targeted Cloud architectures and applications. Heuristics-based algorithms, such as MMT algorithms, suffer from high variance and poor convergence because of their greedy approach. We propose an online reinforcement learning algorithm called Megh. Megh does not require prior knowledge of the workload rather learns the dynamics of workloads as-it-goes. Megh models the problem of energy- and performance-efficient resource management during live migration as a Markov decision process and solves it using a functional approximation scheme. While several reinforcement learning algorithms are proposed to solve this problem, these algorithms remain confined to the academic realm as they face the curse of dimensionality. They are either not scalable in real-time, as it is the case of MadVM, or need an elaborate offline training, as it is the case of Q-learning. These algorithms often incur execution overheads which are comparable with the migration time of a VM. Megh overcomes these deficiencies. Megh uses a novel dimensionality reduction scheme to project the combinatorially explosive state-action space to a polynomial dimensional space with a sparse basis. Megh has the capacity to learn uncertain dynamics and the ability to work in real-time without incurring significant execution overhead. Megh is both scalable and robust. We implement Megh using the CloudSim toolkit and empirically evaluate its performance with the PlanetLab and the Google Cluster workloads. Experiments validate that Megh is more cost-effective, converges faster, incurs smaller execution overhead and is more scalable than MadVM and MMT. An empirical sensitivity analysis explicates the choice of parameters in experiments.

Multi-Dimensional Regression Host Utilization algorithm (MDRHU) for Host Overload Detection in Cloud Computing

The use of cloud computing data centers is growing rapidly to meet the tremendous increase in demand for high-performance computing (HPC), storage and networking resources for business and scientific applications. Virtual machine (VM) consolidation involves the live migration of VMs to run on fewer physical servers, and thus allowing more servers to be switched off or run on low-power mode, as to improve the energy consumption efficiency, operating cost and CO2 emission. A crucial step in VM consolidation is host overload detection, which attempts to predict whether or not a physical server will be oversubscribed with VMs. In contrast to the majority of previous work which use CPU utilization as the sole indicator for host overload, a recent study has proposed a multiple regression host overload detection algorithm, which takes multiple factors into consideration: CPU, memory and network BW utilization. This paper provides further improvement along two directions. First, we provide Multi-Dimensional Regression Host Utilization (MDRHU) algorithms that combine CPU, memory and network BW utilization via Euclidean Distance (MDRHU-ED) and absolute summation (MDRHU-AS), respectively. This leads to improved results in terms of energy consumption and service level agreement violation. Second, the study explicitly takes real-world HPC workloads into consideration. Our extensive simulation study further illustrates the superiority of our proposed algorithms over existing methods. In particular, as compared to the most recently proposed multiple regression algorithm that is based on Geometric Relation (GR), our proposed algorithms provide an improvement of at least 12% in energy consumption, and an improvement of at least 80% in a metric that combines energy consumption, service-level-violation, and number of VM migrations.

Towards Fast and Lightweight Checkpointing for Mobile Virtualization Using NVRAM

Checkpointing is a key enabler of hibernation, live migration and fault-tolerance for virtual machines (VMs) in mobile devices. However, checkpointing a VM is usually heavyweight: the VM's entire memory needs to be dumped to storage, which induces a significant amount of (slow) I/O operations, degrading system performance and user experience. In this paper, we propose FLIC, a fast and lightweight checkpointing machinery for virtualized mobile devices by taking advantages of recent byte-addressable, non-volatile memory (NVRAM). Instead of saving the VM's entire memory to storage, we store its working set pages in NVRAM, avoiding accessing slow flash memory (compared to server-grade SSDs). To further reduce the write activities to flash memory, we propose an energy-efficient data deduplication to eliminate redundant data in VM snapshot and save storage space. Experimental results based on an Exynos 5250 SoC show that our approach can effectively improve the performance of checkpointing in mobile virutalization and save energy.

Selection Strategy for VM Migration Method

Virtual machine (VM) live migration occurs frequently in cloud environments. When it is necessary to migrate many VMs, minimizing the migration time is an important concern. Thus, choosing an appropriate strategy to perform VM migration is essential. Accordingly, this paper discusses the advantages and disadvantages of four different migration methods and evaluates their respective migration times and throughputs.

Comparative Analysis of Various Techniques of VM Live Migration in Cloud Computing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.