Virtual Machine Migration Techniques Research Articles

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.

Affinity based VM Migration: Taxonomy and Challenges

using virtualization many Virtual Machines can run parallel on the same Host. For dynamic resource management, virtual machines can be migrated from residing host to a different. But before starting the migration some questions need to be answered like when to start the virtual machine migration, which VM to migrated and where? The Virtual Machine migration methods on the Virtual cloud environment has already been researched at length, but very few studies have focused on affinity-relations among virtual machines during migration, hence the key objective of this research paper, is to explore the Affinity-aware VM migration in detail and propose Affinity-Aware VM migration algorithms for migration of a group of VMs with affinity to a destination Host with less capacity than required. This paper also provides a brief review of several virtual machine migration techniques.

Toward an integrated dynamic defense system for strategic detecting attacks in cloud networks using stochastic game

In a complex network as a cloud computing environment, security is becoming increasingly based on deception techniques. To date, the static nature of cyber networks offers to adversaries good opportunities to systematically study the network environment, launch a cyber-attack effortlessly and wide-spread and finally defeat the target system. In order to resolve the limitations of the traditional security measures as intrusion prevention or detection systems, firewall, access list, etc., which did not change the attack surface and cannot avoid zero-days attacks, technics that provide dynamic defense, such virtual machine migration and honeypot should be deployed. Despite this, with a virtual machine migration technique, not all virtual machines’ migration between servers enhances security considerably. In this paper, we propose an integrated defense system combining virtual machine migration and honeypot. The effectiveness of the proposed system is discussed in terms of security policies. In addition, our proposed model determines the potential attack paths quantitatively then classifies them into two sub-sets: attack paths explored only and attack paths explored and exploited based on the black box intrusion steps. Thus, to model the interaction attacker–defender, the attack graph combined with the stochastic game theory is used. Finally, we carry out some numerical results to demonstrate the effectiveness of the proposed security game model.

VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques

Green cloud is a catchphrase in today’s IT industry and hence energy efficiency in cloud computing is one of the most significant parameters to follow nowadays to evaluate the efficiency of the cloud service. It is a driving force for adaptability of a cloud computing service in recent era. For a highly commercial service like cloud, maintaining the QoS parameters and keeping the service availability and service quality highly optimized to get the competitive advantage, cloud data centers are almost available on a 24x7 basis ; which in turn is a reson for high power consumption. So it is very much necessary to maintain a balance between power and quality of the service. One feasible solution for achieving energy efficiency is Virtual Machine migration technique in real time or when they are in turned off condition. This paper discusses about several VM Migration techniques and analyses their perspectives.

Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center

Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation.

Impact of Factors Affecting Pre-copy Virtual Machine Migration Technique for Cloud Computing

Abstract Cloud managers trigger Virtual Machine (VM) migration to achieve the challenges of server consolidation, load balancing, resource management, and server fault situation. However, the performance of VM migration is affected by VM size, running application’s dirty rate and the number of migration iterations. Therefore, to measure VM migration performance by considering all these parameters becomes a challenging issue. The main contribution of this study is to evaluate the performance impact of these migration control parameters. Simulation results demonstrate that: (i) Migration time and downtime increases of two orders of magnitude while increasing the VM size, (ii) As the application dirty rate increases the migration time and downtime also increases, (iii) Half iterations of pre-copy migration are enough to reduce the migration time and downtime. In particular, the results obtained can help cloud service providers to find the performance impact of these factors on VM migration technique and meet the required goals of efficient deployment of cloud services.

Fault Tree Analysis based Virtual Machine Migration for Fault-Tolerant Cloud Data Center

Though cloud data center is highly adapted for flexible, scalable and highly available computing and storage resources, it is vulnerable to failures. Predicting the occurrence of server failure and taking appropriate preventive measures are essential in the cloud data center. In order to develop fault-tolerant cloud data center, a Fault Tree Analysis (FTA) based failure aware virtual machine (VM) migration technique is proposed. A fault tree is constructed for server failure event. The server failure is estimated by analyzing the fault tree and the virtual machines are migrated proactively to an alternate server before the failure. Simulations have been carried out and performance of the proposed technique is analyzed in terms of throughput. The results show that the proposed technique outperforms the other state of art techniques.

Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.

Workload aware VM consolidation method in edge/cloud computing for IoT applications

Wide-ranging edge cloud data centers are a vital part of the solution for the problems caused by enormous growth in the IT industry for high computational power by advanced service applications. Majority of IoT applications switched to the Cloud and this stimulated the emergence of Edge technology to better manage the computing applications, data, resource and services. Consequently, with the massive client size and enormous applications trying to benefit from the cloud service, it makes it a challenging task for the edge cloud data centers to work in a power saving mode. In this paper, we propose a virtual machine consolidation method to switch the idle physical servers into hibernation mode, resulting in reduced power usage. We know that edge cloud data centers offer storage as a service, in this study we address the issues pertaining to storage units in the data centers. A unique classification approach is adopted to ensure load is balanced accordingly during allocation and our main contribution is on the VM migration technique. The VM migration is aimed at consolidating the VMs based on the workload to reduced number of physical machines to mitigate the energy consumption and promoting green computing. Therefore, we name the approach as Workload Aware Virtual Machine Consolidation Method (WAVMCM). We validate the proposed method with a competitive analysis of experimental results gathered from comparing it with Artificial Intelligence based probabilistic algorithm like Simulated Annealing, Genetic Algorithm and a case of no migration. Experimental results demonstrate that the proposed WAVMCM reduces 9% active servers saving 15% of power consumption when compared to genetic algorithm based method.

A Review on Different Types of Live VM Migration Methods with Proposed Pre-Copy Approach

Cloud computing is being considered as the future architecture of IT world. Virtualization creates logical resources from physical resources which are allocated with flexibility to applications. Server virtualization is a technique for the division of the physical machine into many Virtual Machines; every Virtual Machine has the capacity of applications execution similar to physical machine. The capability of Virtual Machine migration i.e. dynamic movement of Virtual Machines between physical machines is achieved by virtualization. Migration techniques differ w.r.t order of state transfer. Pre-copy migration method transfers all pages of memory from source to destination while Virtual Machine is executing on source. Post-copy migration is transfer of memory. content after the transfer of process state. Specially, post-copy migration, first copied the process states to the destination machine. Total time of migration, Total pages transferred and Downtime are important parameters considered during live Virtual Machine migration. Many improved live pre copy Virtual Machine migration techniques tries to decrease all the three above mentioned parameters. Proposed approach also tries to minimize all the three performance parameters.

Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

Currently, cloudlet based wireless metropolitan area network (WMAN) is emerging as an effective paradigm to extend the performance of mobile devices, which enables the execution of computational intensive mobile applications. But the normal operation of cloudlets consumes a large amount of energy, which brings about carbon dioxide emissions, aggravation of the greenhouse effect, etc. Meanwhile the data transmission of various mobile users among cloudlets would cause leakage of personal privacy. In view of this challenge, we propose an energy-efficient cloudlet management method, named ECM, for privacy preservation in WMAN. Technically, an optimization model is designed to formalize our problem. Then, based on live virtual machine (VM) migration technique, a corresponding privacy-aware VM scheduling method for energy saving is designed to determine which VMs should be migrated and where they should be migrated. Finally, experimental data demonstrate that the proposed method is both efficient and effective.

A novel energy efficient virtual machine configuration and migration technique

The recent growth in the data centre usage and the higher cost of managing virtual machines clearly demands focused research in reducing the cost of managing and migrating virtual machines. The cost of virtual machine management majorly includes the energy cost, thus the best available virtual machine management and migration techniques must have the lowest energy consumption. The management of virtual machine is solely dependent on the number of applications running on that virtual machine, where there is a very little scope for researchers to improve the energy. The second parameter is migration in order to balance the load, where a number of researches are been carried out to reduce the energy consumption. This work addresses the issue of energy consumption during virtual machine migration and proposes a novel virtual machine migration technique with improvement of energy consumption. The novel algorithm is been proposed in two enhancements as VM selection and VM migration, which demonstrates over 47% reduction in energy consumption.

Enabling green computing in cloud environments: Network virtualization approach toward 5G support

AbstractVirtualization technology has revolutionized the mobile network and widely used in fifth‐generation innovation. It is a way of computing that allows dynamic leasing of server capabilities in the form of services like Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The proliferation of these services among the users led to the establishment of large‐scale cloud data centers that consume an enormous amount of electrical energy and results into high metered bill cost and carbon footprint. In this paper, we propose three heuristic models, namely, median migration time, smallest void detection, and maximum fill technique, that can reduce energy consumption with minimal variation in service‐level agreements negotiated. Specifically, we derive the cost of running cloud data center, cost optimization problem, and resource utilization optimization problem. Power consumption model is developed for cloud computing environment focusing on linear relationship between power consumption and resource utilization. A virtual machine (VM) migration technique is considered, focusing on synchronization‐oriented shorter stop‐and‐copy phase. The complete operational steps as algorithms are developed for energy‐aware heuristic models including median migration time, smallest void detection, and maximum fill technique. To evaluate the proposed heuristic models, we conduct experimentations using PlanetLab server data often in ten days and synthetic workload data collected randomly from the similar number of VMs employed in PlanetLab servers. Through evaluation process, we deduce that the proposed approaches can significantly reduce the energy consumption, total VM migration, and host shutdown while maintaining high system performance.

Resource-aware virtual machine migration in IoT cloud

Internet of Things (IoT) is a promising paradigm enabling many applications to network together through the internet. A huge volume of data is generated by such IoT applications for computation, storage, and analytics through the infrastructure and platform as service offered by cloud computing. Placement and execution of IoT applications in the cloud is a challenging task. In cloud-based IOT application, sudden changes in the sensing environment cause spikes of data flowing into the cloud. This causes resource starvation in the virtual machine and initiates migration of virtual machine from one physical server to other. However, unplanned migration causes a severe performance degradation to the application running on the cloud. Selection of suitable destination server for the virtual machine during migration is an important concern. This paper proposes a resource-aware virtual machine migration technique. Any sudden change in the sensing environment is observed by clustering the servers. The suitable target server is selected based on the resource utilization and job arrival rate of the destination server. The proposed technique is implemented in cloud platform running analytics on smart agriculture application. The evaluation results show that the proposed method outperforms the state of art techniques in terms of the number of migrations, energy utilization and migration time.

Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

The incoming era of the fifth-generation fog computing-supported radio access networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of virtual machines (VMs) toward nearby fog data centers. For this purpose, the bandwidths of the multiple wireless network interface cards of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena, the energy consumptions of the current state-of-the-art VM migration techniques may still offset their expected benefits. Motivated by these considerations, in this paper, we analytically characterize and implement in software and numerically test the optimal minimum-energy settable-complexity bandwidth manager (SCBM) for the live migration of VMs over 5G FOGRAN MPTCP connections. The key features of the proposed SCBM are that: 1) its implementation complexity is settable on-line on the basis of the target energy consumption versus implementation complexity tradeoff; 2) it minimizes the network energy consumed by the wireless device for sustaining the migration process under hard constraints on the tolerated migration times and downtimes; and 3) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the wireless environment without requiring forecasting. The actual effectiveness of the proposed SCBM is supported by extensive energy versus delay performance comparisons that cover: 1) a number of heterogeneous 3G/4G/WiFi FOGRAN scenarios; 2) synthetic and real-world workloads; and, 3) MPTCP and wireless connections.

Inspection of Trust Based Cloud Using Security and Capacity Management at an IaaS Level

Cloud Computing is an example of the distributed system where the end user has to connect to the services given by the cloud which is maintained by the cloud service provider (CSP). The user has to have certain trust upon the cloud as finally, the end user has to migrate the jobs into the cloud of some third party, as the on-premises data or sources are to be kept across the globe, the CSP have to maintain the trust level so that the end user can opt for the services given by the certain trusted Cloud. Ultimately there will be various elements of levels happening at the CSP side to maintain the trust level, like the safety features for security has to be identified, federation related or Virtual Machine migration techniques status has to be always monitored to maintain and avoid certain uncertainty which will affect the trust level of the cloud, which can lead to the compromised situation in between the end user and CSP, as a result the trust value will decrease, In this paper we are proposing a techniques where the security features and conditions for load balancing monitoring technique with proactive actions will be analyzed to maintain the specified trust level.

An Adaptive Overload Detection Policy Based on the Estimator Sn in Cloud Environment

Efficient use of cloud resources and providing QoS to its clients is quite challenging for cloud service providers. On one hand, deployment of excessive active resources leads to increase in operational cost and on the other hand, shortage of resources may affect the QoS and SLA violations. In order to optimize the resource utilization of datacenter keeping SLA intact, the issues like over-loaded and under-loaded servers in a cloud datacenter are very important to deal with. Virtual machine migration technique is quite effective in handling such issues. The present work focuses on the adaptive threshold based overload detection policy which uses the robust estimator Sn for statistically analyzing the historical CPU usage of hosts, periodically and accordingly adjusts the upper CPU utilization threshold. The results obtained from proposed policy are compared with Median Absolute Deviation policy for overload detection and it has been found that energy performance efficiency of proposed policy is better than the median absolute deviation policy.

Energy-Efficient Load-Aware VM Placement using Multi-Metrics Analysis

Objective: Cloud data centres comprise of a huge number of physical machines which host a number of heterogeneous virtual machines. These VMs exhibit fluctuating behaviour in terms of resource usage and specifications leading to disproportionate resource utilization in physical machines. This load imbalance on a host degrades its performance and violates many SLA features. To maintain even load in a data centre, an effective virtual machine placement technique is required. This paper proposes an efficient VM placement and migration technique based on three-tier architecture and considers load as a prime objective. Method: To maintain an even-load in a data centre, many load balancing techniques have been suggested which consider either migration cost or heterogeneity as the overall objective. This paper uses a multi-metrics analysis to distribute VMs evenly and maintains a stable equilibrium inside a data centre. We have applied Analytical Hierarchy Process (AHP) for efficient virtual machine placement using four post placement metrics which defines some of the key Service Level Agreement (SLA) parameters. The metrics considered for placement and migration are all load-centric and promise fewer migrations and SLA violations. Findings: Simulation results show a remarkable reduction in migrations which improves energy conservation inside the data centre. Application of AHP in balancing a data centre’s load is still unexplored. The presented placement technique selects the best candidate machine for placement, hence upgrades the performance. Improvement: Further enhancement includes adding more metrics for placement to fine tune performance and ensure better resource utilization along with reduction in overall energy consumption of a data centre.

Deferred virtual machine migration

The rapid growth in demand for cloud services has led to the establishment of huge virtual machines. Online virtual machine (VM) migration techniques offer cloud providers means to reduce power consumption while keeping quality of service. However, the efficiency of online migration is suboptimal since it is degraded by the execution of redundant migrations. To alleviate this problem, we introduce a load-aware VM migration technique. The key idea is to defer these migrations and perform a quick analysis of the loads on target servers before launching any migration. This consolidation is applied in two steps: first all migrations are tested and a set of candidates that are suspected to be redundant are formed and postponed for a short time. Then, the servers are analysed and only a subset of the migration candidates is activated. Our selection mechanism is conservative in the sense that it avoids selecting and activating VM migrations that are unlikely to cause a harmful overloading on the target servers. Taking this conservative migration policy leads to an overall effective execution of virtual machines. Our experiment results demonstrate the usefulness and effectiveness of our method.