Virtual Machine Migration Techniques Research Articles

Continuous-Observation One-Sided Two-Player Zero-Sum Partially Observable Stochastic Game With Public Actions

The one-sided two-player zero-sum partially observable stochastic games (OTZ-POSG) have emerged as popular models recently, especially in the cyber-security literature. In this game, the state is hidden for one player but directly observed by the other player for whom we call the informed player. All existing OTZ-POSG models assumed that the observation space is discrete and the action of the informed player is private. However, these assumptions may become invalid in real applications. For example, for the virtual machine migration techniques in moving target defenses, the observed traffic data of a network is in a continuous space and the switching strategy of the defender can be inferred by the attacker. This paper, therefore, proposes a continuous-observation OTZ-POSG with public actions and studies the existence of its equilibrium. The main challenge induced by the public action is the potential information leakage–the action of the informed player could reveal state information because his or her policy is state-dependent. To solve this issue, we adopt a two-step belief update strategy for players and prove the existence of a Stackelberg equilibrium. We show that the game can be solved iteratively through value iteration. However, calculating the exact value function is impractical as the observation space is continuous. To mitigate the computational complexity issue, we propose a point-based approximation algorithm to approximate the exact value function and meanwhile extend the dynamic partitioning approach to discretize the observation space into finite discrete partitions. We show that the value function of the leader can be approximated by a piece-wise linear and concave function with an error bound. Finally, examples are used in each section to illustrate ideas of the proposed algorithms.

Comprehensive Analysis of VM Migration Trends in Cloud Data Centers

Background: Virtualization adequately maintains increasing requirements for storage, networking, servers, and computing in exhaustive cloud data centers (CDC)s. Virtualization assists in gaining different objectives like dedicated server sustenance, fault tolerance, comprehensive service availability, and load balancing, by virtual machine (VM) migration. The VM migration process continuously requires CPU cycles, communication bandwidth, memory, and processing power. Therefore, it detrimentally prevails over the performance of dynamic applications and cannot be completely neglected in the synchronous large-scale CDC, explicitly when service level agreement (SLA) and analytical trade goals are to be defined. Introduction: Live VM migration is intermittently adopted as it grants the operational service even when the migration is executed. Currently, power competence has been identified as the primary design requirement for the current CDC model. It grows from a single server to numerous data centres and clouds, which consume an extensive amount of electricity. Consequently, appropriate energy management techniques are especially important for CDCs. Method: This review paper delineates the need for energy efficiency in the CDC, the systematic mapping of VM migration methods, and research pertinent to it. After that, an analysis of VM migration techniques, the category of VM migration, duplication, and context-based VM migration is presented along with its relative analysis. Results: The various VM migration techniques were compared on the basis of various performance measures. The techniques based on duplication and context-based VM migration methods provide an average reduction in migration time of up to 38.47%, data transfer rate of up to 51.4%, migration downtime of up to 36.33%, network traffic rate of up to 44% and reduced application efficiency overhead up to 14.27%. Conclusion: The study aids in analyzing threats and research challenges related to VM migration techniques which ultimately help in exploring future research directions that would help aspiring cloud professionals.

A machine learning model for improving virtual machine migration in cloud computing

Cloud Computing is a paradigm allowing access to physical and application resources online via the Internet. These resources are virtualized using virtualization software to make them available to users as a service. Virtual machines (VMs) migration technique provided by virtualization technology impacts the performance of the cloud. It is a significant concern in this environment. When allocating resources, the distribution of VMs is unbalanced, and their movement from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper addresses the VMs migration issue by applying a machine learning model to reduce the VMs migration number and energy consumption. The proposed algorithm (named VMLM) is based on improving VM’s migration process and selection. It has been benchmarked with JVCMMD and EVSP solutions. The simulation results demonstrate the efficiency of our proposal, which includes two phases the machine learning preparing stage and the VMs migration stage.

A data transmission approach with energy reduction based on virtual machine migration technique in cloud computing

A data transmission approach with energy reduction based on virtual machine migration technique in cloud computing

Virtual Machine Migration Techniques for Optimizing Energy Consumption in Cloud Data Centers

Virtual Machine Migration Techniques for Optimizing Energy Consumption in Cloud Data Centers

Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center

The demand for digitization has inspired organizations to move towards cloud computing, which has increased the challenge for cloud service providers to provide quality service. One of the challenges is energy consumption, which can shoot up the cost of using computing resources and has raised the carbon footprint in the atmosphere; therefore, it is an issue that it is imperative to address. Virtualization, bin-packing, and live VM migration techniques are the key resolvers that have been found to be efficacious in presenting sound solutions. Thus, in this paper, a new live VM migration algorithm, live migration with efficient ballooning (LMEB), is proposed; LMEB focuses on decreasing the size of the data that need to be shifted from the source to the destination server so that the total energy consumption of migration can be reduced. A simulation was performed with a specific configuration of virtual machines and servers, and the results proved that the proposed algorithm could trim down energy usage by 18%, migration time by 20%, and downtime by 20% in comparison with the existing approach of live migration with ballooning (LMB).

Memory/Disk Operation Aware Lightweight VM Live Migration

Live virtual machine migration technique allows migrating an entire OS with running applications from one physical host to another, while keeping all services available without interruption. It provides a flexible and powerful way to balance system load, save power, and tolerate faults in data centers. Meanwhile, with the stringent requirements of latency, scalability, and availability, an increasing number of applications are deployed across distributed data-centers. However, existing live migration approaches still suffer from long downtime and serious performance degradation in cross data-center scenes due to the mass of dirty retransmission, which limits the ability of cross data-center scheduling. In this paper, we propose a system named Memory/disk operation aware Lightweight VM Live Migration across data-centers with low performance impact (MLLM). It significantly improves the cross data-center migration performance by reducing the amount of dirty data in the migration process. In MLLM, we predict disk read workingset (i.e., more frequently read contents) and memory write workingset (i.e., more frequently write contents) based on the access sequence traces. And then we adjust the migration models and data transfer sequence by the workingset information. We further proposed an improved algorithm for workingset estimation. Moreover, we discussed the potential use of machine learning (ML) to enhance the performance of the VM migration and also propose a two-level hierarchical network model to make the ML-based prediction more efficient. We implement MLLM and its improved versions on the QEMU/KVM platform and conduct several experiments. The experimental results show that 1) MLLM averagely reduces 62.9% of total migration time and 36.0% service downtime over existing methods; 2) The improved workingset estimation algorithm reduces 9.32% memory pre-copy time on average over the original algorithm.

Analysis of Data and Virtualization Security in Cloud

Cloud computing is getting attention of the researchers these days. Large number of users are attached with cloud computing. But as there are several merits of cloud computing some demerits are also there. The one of the greatest threat in cloud computing is security. This security is concerned with data of the users that is present in cloud and is transferred across the clouds and the virtualization concept. In this paper several attacks on cloud are studied and solutions to those threats are compiled. In this paper a secure technique to handle data in cloud and secure virtualization is proposed in this paper. A secure VM migration technique which gives VM isolation guarantee and VM escape protection is proposed here. And it is shown that this security algorithm possess no considerable overhead.

Hybrid Optimization Algorithm for VM Migration in Cloud Computing

The number of cloud users and their respective workload increases everyday with the inherent benefits of cloud computing. On the other hand, it becomes critical for service providers to maintain Quality of Service (QoS) even under heavy workload conditions. In order to provide better computing services, cloud utilizes Virtual Machine (VM) migration techniques, which eases the process of providing the services to the user without any delay and with minimum energy consumption. The existing cloud computing services mainly rely on migration techniques; nevertheless, handling large VM migrations consumes more energy, which directly affects the VM performances. This necessitates the need to develop an effective VM migration strategy to perform the necessary migrations and avoid unnecessary migrations. Conventional migration techniques perform migration based on static parameters which attains less efficient results and lags in performance while handling the resource utilization. In this research work, a hybrid optimization algorithm is presented to handle VM migration in a cloud environment. Cuckoo search optimization algorithm and particle swarm optimization algorithm are combined to obtain the proposed hybrid optimization model. The major objective of this research work is to reduce energy consumption, computation time, and migration cost. Maximizing resource utilization is another objective of this research work. To validate the research objective, the performance of hybrid optimization model is verified through simulation analysis and compared with conventional algorithms like firefly optimization, whale optimization, hybrid whale optimization, and hybrid bee colony optimization in terms of energy consumption, migration cost, resource availability, and computation time.

A Survey on Virtual Machine Migration in Cloud Computing

Cloud computing provides multiple services to users through the internet and these services include cloud storage, applications, servers, security and large network access. Virtual Machine allows the user to emulate multiple operating systems on a single computer; with the help of virtual machine migration users can transfer operating system instances from one computer to multiple computer machines. In this paper we will be discussing VM migration in cloud and also I will explain the whole procedure of VM migration. The two methods through which we can perform VM migration are Live VM migration and NON-live VM migration.VM migration also helps in managing the loads of the multiple machines and with VM we can save power consumption. People have written about cloud computing and virtual machines in previous studies, but in this research, we'll speak about virtual machine migration in cloud computing, as well as the techniques that are used in the VM migration process. I have used table to show the differences between VM migration techniques.

IAGA: Interference Aware Genetic Algorithm based VM Allocation Policy for Cloud Systems

Diversified systems hosted on cloud infrastructure have to work increasingly on physical servers. Cloud applications running on physical machines require diverse resources. The resource requirements of cloud applications are fluctuating based on the resource intensity of the applications. The multi-tenancy of Cloud servers can be achieved based on effective resource utilization. The optimum resource utilization, maximum service level agreement, and minimization of interference are the major objectives to be achieved. Using live Virtual Machine (VM) migration techniques cloud resources can be utilized efficiently. But the migrated VMs can interfere with the ongoing applications on the targeted server which may lead to the service level agreement violation (SLAV) and performance degradation. To resolve this issue, understanding the current state of cloud hosts before the allocation of newly migrated VM is necessary. This paper presents Interference Attentive Genetic Algorithm (IAGA) based VM allocation strategy to achieve the aforementioned objectives. The proposed IAGA policy has outperformed existing policies for quantifiable performance metrics such as energy consumed by cloud systems, count of hosts shut down, average SLAV, and count of VM migrations.

Proactive Stateful Fault-Tolerant System for Kubernetes Containerized Services

Recently, the development of Kubernetes (K8s) containerization platform has enabled cloud-based, lightweight, highly scalable, and agile services in both general and telco use-cases. Ensuring high availability, reliable and continuous containerized services is a major requirement of service providers to provide fault-tolerance, transparent service experiences to end-users. To satisfy this requirement, fault prediction and proactive stateful service recovery features must be applied in cloud systems. Prior proactive failure recovery approaches mostly focused on either improving fault prediction performance based on different machine learning time series forecasting techniques or optimizing recovery service placement after fault prediction. However, a mechanism that enables stateful containerized service migration from the predicted faulty node to the healthy destination node has not been studied. Service migration in previous proactive works is only simulated or performed by virtual machine (VM) migration techniques. In this paper, we propose a proactive stateful fault-tolerant system for K8s containerized services that pipelines a Bidirectional Long Short-Term Memory (Bi-LSTM) fault prediction framework and a novel K8s stateful service migration mechanism for service recovery. Experimental results show how the Bi-LSTM model improved prediction performance against other time-series forecasting models in prior proactive works. We then combined the Bi-LSTM fault prediction framework with both the default K8s and our stateful migration mechanisms. The comparison between these two proactive systems proves our system efficiency in terms of avoiding Quality of Service (QoS) violation.

An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

Cloud Computing provides various services to the customer in a flexible and reliable manner. Virtual Machines (VM) are created from physical resources of the data center for handling huge number of requests as a task. These tasks are executed in the VM at the data center which needs excess hosts for satisfying the customer request. The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time. This process is carried out based on various algorithms which follow a predefined capacity of source VM leads to the capacity issue at the destination VM. The proposed VM migration technique performs the migration process based on the request of the requesting host machine. This technique can perform in three ways namely single VM migration, Multiple VM migration and Cluster VM migration. Common Deployment Manager (CDM) is used to support through negotiation that happens across the source host and destination host for providing the high quality service to their customer. The VM migration requests are handled with an exposure of the source host capabilities. The proposed analysis also uses the retired instructions with execution by the hypervisor to achieve high reliability. The objective of the proposed technique is to perform a VM migration process based on the prior knowledge of the resource availability in the target VM.

A Virtual Machine Migration Mechanism Based on Firefly Optimization for Cloud Computing

Background: Cloud computing is one of the prominent technology revolutions around us. It is changing the ways the consumer expends services, changing the ways the organization develop and run applications and is completely reshaping the old business models in multiple industries. Cloud service providers need large-scale data centers for offering cloud resources to users, the electric power consumed by these data centers has become a concrete and prudential concern. Most of the energy is dissipated in these data centers due to under-utilized hosts, which also subsidies to global warming. The broadly adept technology is virtual machine migration in cloud computing, therefore, the main focus is to save energy. Objective: Virtual Machine (VM) migration can reap various objectives like load balancing, ubiquitous computing, power management, fault tolerance, server maintenance, etc. This paper presents an energy-oriented mechanism for VM migration based on firefly optimization that reduces energy consumption and the number of VM migrations to a great extent. Method: A Firefly Optimization (FFO) oriented VM migration mechanism has been proposed, which allocates tasks to the physical machines in cloud data centers. It strives to migrates high loaded VMs from one physical node to another, which induces minimum energy consumption after VM migration. Results: The empirical result shows that the FFO based mechanism, implemented in the CloudSim simulator, performs better in terms of the number of hosts saved up to 13.91% in contrast to the First Fit Decreasing (FFD) algorithm and 8.21% as compared to Ant Colony Optimization (ACO). It reduced energy consumption up to 12.76% as compared to FFD and 7.78% as compared to ACO and, ultimately lesser number of migrations up to 52.49% when compared to FFD and 44.51% as compared to ACO. Conclusion: The proposed scheme performs better in terms of saving hosts, reducing energy consumption, and decreasing the number of migrations in contrast to FFD and ACO techniques. The research paper also presents challenges and issues in cloud computing, VM migration process, VM migration techniques, their comparative review as well.

An Effective Ant Colony Optimization Methodology For Virtual Machine Placement In Cloud Data Centre

A Cloud environment is a type of service-oriented computer application, and its services comprise SaaS, IaaS, PaaS, and XaaS. The cloud user chooses essential services from dust, and those service models fulfill customers' incoming tasks with service contracts. Each task is executed in Physical Machine (PM) deployed in the cloud through Virtual Machine (VM), so VM plays a vital role in the cloud environment. A cloud would be a pay as you go platform, and hence many VMs within the physical servers can be created and destroyed. The effective allocation of virtual machines to physical machines permits the economic sharing of physical devices to obtainable data centers. These allocation strategies facilitate measuring and enhancing the performance of the cloud. The focus of this work is to put forward a VM migration technique in the cloud environment, which improves the CPU and memory utilization with reduced traffic congestion in the cloud data centers using bioinspired algorithms. The proposed approach improves the memory management, reduces the traffic, and maximizes CPU utilization the current state of the VMs and PMs is not interrupted.

Auto-scaling techniques for IoT-based cloud applications: a review

Cloud and IoT applications have inquiring effects that can strongly influence today’s ever-growing internet life along with necessity to resolve numerous challenges for each application such as scalability, security, privacy, and reliability. During the deployment of IoT-based Cloud applications, the demand for Cloud tenants is dynamic that makes challenging to maintain scalability of the system. Developing an effective scaling technique is not merely a big concern, but how to achieve autonomic scaling results using future load prediction and migration policies is also a crucial phase. Also, to evaluate such auto-scaling strategy, certain Quality of Service (QoS) metrics must be recognized, explored and leveraged to enhance the performance of the system. Therefore, in this paper, a survey of existing auto-scaling, load prediction and VM migration techniques for IoT-based Cloud applications has been carried out along with the evaluation of various QoS parameters. Further, the future trends have also been discussed for performing auto-scaling in a Cloud environment.

An intelligent surveillance video analytics framework using NACT-Hadoop/MapReduce on cloud services

Video analytics has gradually increased in recent years. The intelligent CCTV cameras in public places, you-tube videos, etc. generate an enormous amount of video data. Generally, video analytics required more time as it contains several processes like encoding, decoding, etc. There are several existing approaches are evolved in improving the efficiency of video analytics but performance delay and loss of data still existing challenges. With our analysis, we strongly state VM migration will be an effective solution to overcome this delay and performance issues. In this paper, we propose NACT based map reducing mechanism (NACT-Map) for processing the real-time streaming videos. The NACT (Novel Awaiting Computation Time) enables the prediction of VM allocation and automatic migration. The scheduling and allocating of the optimal resource are done by task monitor who utilizes the Task manager (TM) system. The NACT based VM migration and MapReduce technique with Hadoop simplifies the process and minimizes the execution time. The splitting of video into chunks of frames speedup the process. Further efficiency is improved by the Map Reduce technique which uses video and its related content for clusters. The performance of our proposed system is executed in the cloudsim with a large dataset contains two real-time videos. Further, the result is compared with the existing methodologies such as distributed video decoding mechanism with extended FFmpeg and VideoRecordReader (VDMFF) (Yoon et al. in Distributed video decoding on Hadoop. IEICE Trans Inf Syst E101-D(1):2933–2941, 2018) and distributed Video Analytics Framework for Intelligent Video Surveillance (SIAT) (Uddin et al. in SIAT: a distributed video analytics framework for intelligent video surveillance. Symmetry 11:911, 2019). The obtained result shows our proposed NACT_Map consumes minimum Task processing time $$({\text{p}}_{{{\text{tix}}}} )$$ and about 90% of efficiency in overall system performance is increased.

A data transmission approach with energy reduction based on virtual machine migration technique in cloud computing

A data transmission approach with energy reduction based on virtual machine migration technique in cloud computing

Hybrid Improved Pre Copy and Post Copy Approach for Virtual Machine Migration in Cloud

With the increasing of rapid demand for cloud resources, load on the cloud server is increased drastically in the past few decades. Live virtual machine (VM) relocation is an important approach for the dynamic scheduling of resources in the cloud environment. The solution for dealing with the dynamic load that transfers the virtual machine from one server to another is virtual machine migration. The cloud provider uses three virtual machine migration techniques such as stop and copy, pre-copy, and post-copy. The pre-copy approach is the most popular approach among all migration approaches but it is not suitable for the write intensive application is running on the virtual machine. This article suggested a hybrid virtual machine solution that would minimize the overall migration time, and downtime. And also there is an increase in the virtual machine's performance. To test the efficiency of the suggested solution, the CloudSim simulation tool is used. Compared to the existing method, the findings of the experiments indicate that the proposed method produces better results.

A Comprehensive Review of Virtual Machine Migration Techniques in Cloud Computing

As cloud users grow exponentially, virtualization of the cloud data center (CDC) is accomplished by running simultaneous virtual machines (VM) on one cloud server in parallel processing. The growing demand for resources such as communication, storage, and computing can be successfully met by using dynamic virtual machine migration. For single or multiple VM migrations, most resource management techniques are used to maximize the performance and efficiency of the cloud data center. The Qos (service quality) declines proportionally with fast increase in cloud users and associated workload. The powerful live VM migration strategy can only be used to deliver better computing facilities, manage large numbers of cloud users and reduce time and energy on cloud data center. Modern cloud computing system depends primarily on live VM migration. This paper analyzes with their advantages and disadvantages all the latest live VM migration techniques. Their future range research and implementation has been briefly mentioned. Comparisons have been made between pre-copy and post-copy VM Migration techniques through simulation corresponding to CPU uses, memory and network as parameters.This paper reviews various virtual machine migration schemes while, through a comprehensive analysis of existing schemes, examining the critical aspects of virtual machine migration schemes.Existing systems are explored with key goals. In the VM migration domain, some open research issues are also mentioned.