Virtual Machine Migration Process Research Articles

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

The purpose of the article is to develop and verify with the help of mathematical modeling a software method of deploying a fault-tolerant computing cluster with a virtual machine, which consists of two physical servers (main and backup), on which a distributed data storage system with synchronous data replication from the source server to the backup server is deployed. For this purpose, the task is to conduct a computational experiment on a model of a fault-tolerant cluster, which neglects costs during recovery for the migration of virtual machines by means of the mathematical application Mathcad. Combining computing resources into clusters is a way to ensure high reliability, fault tolerance, and continuity of the computing process of computer systems. This is achieved through virtualization, which enables the movement of virtual resources, services, or applications between physical servers while maintaining the continuity of computing processes. The focus of this study is on a failover cluster, which is composed of two physical servers (primary and backup) connected through a switch, and each server has a local hard disk. A distributed storage system with synchronous data replication from the source server to the backup server is deployed on the local disks of the servers, and a virtual machine is running on the cluster. Markovian processes, flows of podias, and Kolmogorov's systems of differential equations are built into the mathematical tools of the model of a water cluster. To ensure the continuity of the computing process in case of a failure of the main server, a shadow copy of the virtual machine is launched on the backup server. The reliability of the failover cluster is measured by the coefficient of non-stationary readiness. A Markov model is proposed to assess the reliability of the failover cluster, taking into account the costs of migrating virtual machines and mechanisms that ensure the continuity of the computing process in the cluster in case of a failure of one physical server. The memory migration process maintains two copies of the virtual machine on different physical servers, enabling them to continue working on the other in the event of failure. A simplified model of the failover cluster neglects the cost of migrating virtual machines and provides an upper estimate of reliability. The study shows that the reliability of a failover cluster, as measured by the non-stationary availability factor, is significantly impacted by the virtual machine migration process. The findings of this study can be used to inform decisions about the technology chosen to ensure the failure stability and continuity of the computing process of computer systems with cluster architecture. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculation was performed under the following failure rates of the server, disk, and switch: λ0 = 1,115×10-5 1/h, λ1 = 3,425×10-6 1/h, λ2 = 2,3×10-6 1/h recovery respectively: μ0 = 0,33 1/h, μ1 = 0,171/h, μ2 = 0,33 1/h. The intensity of synchronization of the distributed storage system: μ3 = 1 1/h, μ4 = 2 1/h. The difference of non-stationary cluster availability coefficients is d = К2(t) – К1(t) = 2.7×10-10

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

An Adaptive Mechanism for Virtual Machine Migration in the Cloud Environment

To manage all the operations of data centers resources, virtualization is the effective technique. In virtualization the virtual machine migration is the way by which data center operator can easily adapt the replacement of virtual machine, improves the resource provisioning and any other maintenance function of data center. Despite of this the virtual machine migration scheme is the major challenge to improve the efficiency of data center. This paper proposed a virtual machine migration process which will be responsible to minimize the migration which leads to reduce the execution time.

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 Improved Metaheuristic based Node Localization Technique for Wireless Sensor Networks

Cloud computing (CC) becomes a familiar topic in offering unlimited access to services as well as resources via the Internet. A comprehensive CC management system is needed to collect details of the task processing and ensure proper resource allocation with the accomplishment of Quality of Service (QoS). At the same time, virtual machine (VM) migration is a crucial problem in the CC platform which contributes to energy utilization and resource usage. Therefore, this paper presents a new energy-aware elephant herd optimization-based VM migration (EAEHO-VMM) scheme. The EAEHO-VMM algorithm aims to migrate the VMs and prediction failure VMs. At the initial stage, the EHO algorithm is executed to minimize the energy utilization of the VM migration process in the CC environment. In addition, a support vector machine (SVM) model is applied to identify the failure VMs and allows relocation in an effective way. In order to make sure the better performance of the EAEHO-VMM algorithm, a series of simulations take place, and the results are investigated in terms of different aspects. The experimental outcomes ensured the enhanced VM migration performance of the EAEHO-VMM algorithm over the other techniques.

Enhancing live virtual machine migration process via optimized resource allocation in next generation mobile edge network: A hybrid evolutionary approach

SummaryThe evolution of 5th Generation wireless technology introduced Mobile Edge Computing, where edge servers are placed at the edge of the network, and are associated with evolved Node Base Stations (eNBs). This enables mobile users to offload their resource‐intensive tasks to these servers and improve network performance by reducing end‐to‐end delay. However, frequent user mobility leads to frequent re‐planning of network and increases network load. This demands dynamic Virtual Machine (VM) migration in Mobile Edge paradigm for an improved Quality of Service (QoS). For an enhanced VM migration process, an optimal pair of migrating VMs and destination edge servers needs to be chosen. In this paper, we propose an optimized decision‐making policy that chooses such optimal pairs. Several decision parameters such as average wait time, processing delay, migration delay, transmission power, and processing power are modeled. A profit function is developed using these modeled decision parameters that chooses the optimal pairs. This function is maximized using the proposed hybrid evolutionary algorithm, which combines the advantages of PSO and GA. The pairs are chosen in such a manner, that the selection guarantees high network throughput, reduced service delay, and energy consumption which is reflected in the simulation.

Security and trust preserving inter‐ and intra‐cloud VM migrations

SummaryThis paper focus on providing a secure and trustworthy solution for virtual machine (VM) migration within an existing cloud provider domain, and/or to the other federating cloud providers. The infrastructure‐as‐a‐service (IaaS) cloud service model is mainly addressed to extend and complement the previous Trusted Computing techniques for secure VM launch and VM migration case. The VM migration solution proposed in this paper uses a Trust_Token based to guarantee that the user VMs can only be migrated and hosted on a trustworthy and/or compliant cloud platforms. The possibility to also check the compliance of the cloud platforms with the pre‐defined baseline configurations makes our solution compatible with an existing widely accepted standards‐based, security‐focused cloud frameworks like FedRAMP. Our proposed solution can be used for both inter‐ and intra‐cloud VM migrations. Different from previous schemes, our solution is not dependent on an active (on‐line) trusted third party; that is, the trusted third party only performs the platform certification and is not involved in the actual VM migration process. We use the Tamarin solver to realize a formal security analysis of the proposed migration protocol and show that our protocol is safe under the Dolev‐Yao intruder model. Finally, we show how our proposed mechanisms fulfill major security and trust requirements for secure VM migration in cloud environments.

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.

Formal Analysis of Virtual Machine Migration and Identification of Faults

Infrastructure as a service (IaaS) cloud supports flexible and agile execution of applications by creating virtualized execution environment namely, virtual machines (VMs) with on-demand infrastructural resources. In such environment, VM migration is used as a tool to facilitate system maintenance, load balancing and fault tolerance. The use of VM migration is to establish the portfolio of using dynamic and scalable infrastructure services offered by the service providers. In this paper, we study the VM migration process and investigate the potential faults which can occur during migration. Also, the state changes of a VM throughout its lifetime has been systematically analyzed and modeled as concurrent state machines. The potential faults are presented considering the live migration process of VM and accordingly VM state changes. In addition, a methodology for identifying the migration faults has been presented.

Efficient Routing and Bandwidth Assignment for Inter-Data-Center Live Virtual-Machine Migrations

Using virtualization, service providerscan create an abstraction of the physical servers hosted in their data centers, and run their services directly on these abstract entities, called virtual machines (VMs). As a direct result of service virtualization, services hosted on VMs can be migrated from one data center to another. VMmigration can be performed without or with a minimal service interruption, and in this case we talk about live VM migration. The main issue of live VM migration is that memory pages are modified during the process. Due to this fact, the same memory pages might be transferred several times, thus increasing both the migration duration and the amount of network resources occupied by the VM migration process. Considering the increasing amount of VM migrations carried on in inter-data-center networks, efficient strategies for live-VM-migration bandwidth provisioning are needed. In this paper, we propose and compare various novel routing and bandwidth assignment algorithms for the live migration of VMs in a distributed DC infrastructure, under dynamic traffic conditions. We find that assigning a bandwidth to theVMmigration request in proportion to the path length with the objective of minimizing the amount of resources occupied by the migration request could improve the network performance. In addition,wepresent a comparative analysis of the serial and the parallel migration of multiple VMs.We quantify the impact of the migration strategy (serial versus parallel) on blocking probability, migration duration, downtime, and resource occupation.

Optimised pre-copy live VM migration approach for evaluating mathematical expression by dependency identification

Live virtual machine (VM) migration is widely used technique for resource consolidation and fault tolerance. KVM and Xen use iterative pre-copy approaches which work well in practice for commercial applications. In this paper, we propose a novel idea for pre-copy live VM migration process and present a detailed performance analysis of the migration process. We show that the migration downtime can be reduced significantly when compared with the previously proposed algorithms. The main idea is to identify the independent instructions from the set of instructions that are still executing at source server. These independent instructions are transmitted to the destination server and start executing without waiting for source server to transmit all the instructions. We present a novel algorithm able to attain minimal downtime and minimal total execution time.

Optimised pre-copy live VM migration approach for evaluating mathematical expression by dependency identification

Live virtual machine (VM) migration is widely used technique for resource consolidation and fault tolerance. KVM and Xen use iterative pre-copy approaches which work well in practice for commercial applications. In this paper, we propose a novel idea for pre-copy live VM migration process and present a detailed performance analysis of the migration process. We show that the migration downtime can be reduced significantly when compared with the previously proposed algorithms. The main idea is to identify the independent instructions from the set of instructions that are still executing at source server. These independent instructions are transmitted to the destination server and start executing without waiting for source server to transmit all the instructions. We present a novel algorithm able to attain minimal downtime and minimal total execution time.

A Study on Security Vulnerability on Cloud Platforms

Cloud computing provides an in-built platform to the users with easy and on-demand access to different system level services for creating virtual machines (VM) with efficient utilization of hardware, computing and network resources. This allows users to remotely execute large number of applications across various domains such as, health-care, utility services, e-governance, etc. There are different cloud operating platforms for creation and management of virtual machines through various system or application services. For example, openstack, oracle virtualbox, Microsoft HyperV are widely used operating platform. Virtual machine migration (VMM) is one of such important system services that used for smooth the progress of system maintenance, load management, fault tolerance, secure and safe service offerings. Virtual Machine live migration service transfers an active VM from one physical machine to another across different data centres. It involves a sequence of operations in iteration for migrating execution context of a VM to the destination machine. These operations are dependent on schedule, availability of resources and overall timing constraints. On the other hand, the migration process involves transferring the control data over a communication channel with a shared storage. Due to the above mentioned complexities of VM migration process, any security failures during the migration may cause transfer of incorrect VM instance to a data centre and which in turn may lead to exposure of the data centre to the attackers. This may be manifested as catastrophic system failure, degradation of system performance, load balancing and in long run may result collapse of the data centre. In this paper we studied various security vulnerability in different cloud platforms. These vulnerability will help the security developers to design a secured cloud platform.

A Virtual Machine Migration Algorithm Based on Network Flow Balance

Current research of virtual machine migration strategy mainly focuses on how to reduce the delay of virtual machine migration process but does not pay much attention to the network flow problem caused by the virtual machine migration. Because of the difference caused by Infrastructure Operator's network location makes a different virtual machine migration strategy, which will result in large differences in network traffic. Infrastructure operator's network resources are scarce resources. Therefore, how to reduce the network flow of virtual machine migration is a problem to be studied. In order to reduce network traffic virtual machine migration, this paper proposes a virtual machine migration algorithm (NFBA) based on network flow balance to obtain the minimum scheduling cost. Experimental results show the migration strategy can effectively reduce the communication traffic between the virtual machine clusters within the system and reduce the burden of network and consider workload balance at the same time.

Live Migration for Virtual Machine Based on Kalman Prediction of Dirty Pages

The live migration of virtual machine (VM) is an important technology of cloud computing. Down-time, total migration time and network traffic data are the key measures of performance. Through the analysis of dynamic memory state of a virtual machine migration process, we propose a dirty pages algorithm prediction based on pre-copy to avoid dirty pages re transmission. Experimental results show that, compared with the Xen virtual machine live migration method adopted, our method can at least reduce 15.1% of the total amount of data and 12.2% of the total migration time.

Virtual machine placement in cloud systems through migration process

Cloud computing is an emerging technology that greatly shapes our lives, where users run their jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud service provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs, different VMs on the same PMs could have different job completion times. Meanwhile, the PMs are also heterogeneous. Therefore, different VM placements have different job completion times, and our objective is to minimise the total job completion time of the input VM requests through a reasonable VM placement schedule. This problem is NP-hard, since it can be reduced to a knapsack problem. We propose an off-line VM placement method through an emulated VM migration process, while the on-line VM placement is solved by a real VM migration process. The migration algorithm is a heuristic approach, in which we place the VM to its best PM directly, if this PM has enough capacity. Otherwise, we migrate another VM from this PM to accommodate the new VM, if a pre-specified migration constraint is satisfied. Furthermore, we study a hybrid scheme where a batch is employed to accept upcoming VMs for the on-line scenario. Evaluation results validate the high efficiency of the proposed algorithms.