Live Migration Of Virtual Machines Research Articles

OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments.

The indispensable collaboration of cloud computing in every digital service has raised its resource usage exponentially. The ever-growing demand of cloud resources evades service availability leading to critical challenges such as cloud outages, SLA violation, and excessive power consumption. Previous approaches have addressed this problem by utilizing multiple cloud platforms or running multiple replicas of a Virtual Machine (VM) resulting into high operational cost. This paper has addressed this alarming problem from a different perspective by proposing a novel mathbb {O}nline virtual machine mathbb {F}ailure mathbb {P}rediction and mathbb {T}olerance mathbb {M}odel (OFP-TM) with high availability awareness embedded in physical machines as well as virtual machines. The failure-prone VMs are estimated in real-time based on their future resource usage by developing an ensemble approach-based resource predictor. These VMs are assigned to a failure tolerance unit comprising of a resource provision matrix and Selection Box (S-Box) mechanism which triggers the migration of failure-prone VMs and handle any outage beforehand while maintaining the desired level of availability for cloud users. The proposed model is evaluated and compared against existing related approaches by simulating cloud environment and executing several experiments using a real-world workload Google Cluster dataset. Consequently, it has been concluded that OFP-TM improves availability and scales down the number of live VM migrations up to 33.5% and 83.3%, respectively, over without OFP-TM.

Multi-resource collaborative optimization for adaptive virtual machine placement.

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

Techniques of migration in live virtual machine and its challenges

Cloud computing is the on-demand availability of computer system resources. Most technology industries are moving to the cloud. Cloud structures can be costly for users. Virtualization is used in cloud computing that helps the cloud at a low cost. Migrating virtual machines (VMs) helps to manage computation. Migration of virtual machines is a core feature of virtualization. The technique of migrating a running virtual machine from one physical host to another with minimal downtime is called "live virtual machine migration." This paper discusses the migration technique, i.e., migration before and after copying, and also issues related to live migration. This paper presents a better approach to the VM migration method and future challenges by differentiating from the previous live VM migration method.

SLA-Aware and Energy-Efficient Virtual Machine Placement and Consolidation in Heterogeneous DVFS Enabled Cloud Datacenter

Increasing demand for computational resource as services over the internet has led to the expansion of datacenter infrastructures. Thus, datacenter authorities are striving to adopt optimal power usage schemes to minimize costs, emissions and Service Level Agreement (SLA) violations in their task scheduling for heterogeneous computation centers. One of the most effective strategies to reduce datacenter energy consumption is to maximize the utilization of physical machines and shut down the idle ones. This can be realized through two main algorithms, namely virtual machine placement and virtual machine consolidation. The VM placement method is a dynamic process to put these virtual devices on physical machines. The consolidation technique, however, tries to improve physical machine efficiency through grouping and live migration of dispersed virtual machines on lower number of active physical machine. In this paper, a novel approach is proposed for improving the physical machine efficiency. The approach employs heuristics and meta-heuristic algorithms with eight performance criteria and is implemented on small to medium scale data centers using simulated cloud module. The results indicates that the proposed method showed up to 10.3%, 5.3%, and 12.5% the more significant efficiency rather best previous algorithms, respectively, in terms of the energy consumption, number of SLA violation and number of VMs migration.

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.

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

Modern networks are built to be linked, agile, programmable, and load-efficient in order to overcome the drawbacks of an unbalanced network, such as network congestion, elevated transmission costs, low reliability, and other problems. The many technological devices in our environment have a considerable potential to make the connected world concept a reality. The Internet of Things (IoT) is a research community initiative to bring this idea to life. Cloud computing is crucial to making it happen. The load balancing and scheduling significantly increase the possibility of using resources and provide the grounds for reliability. Even if the intended node is under low or high loading, the load balancing techniques can increase its efficiency. This paper presents a scheduling technique for optimal resource allocation with enhanced particle swarm optimization and virtual machine live migration technique. The proposed technique prevents excessive or low server overloads through optimal allocation and scheduling tasks to physical servers. The proposed strategy was implemented in the cloudsim simulator environment and compared and showed that the proposed method is more effective and is well suited to decreasing execution time and energy consumption. This solution provides grounds to reduce energy consumption in the cloud environment while decreasing execution time. The simulation results showed that the amount of energy consumption compared to particle crowding has decreased by 10% and compared to PSO (Particle Swarm Optimization) scheduling by more than 8%. Also, the execution time has been reduced by 18% compared to particle swarm scheduling and by 8% compared to PSO.

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud.

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.

Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

A prediction-based model for virtual machine live migration monitoring in a cloud datacenter

A prediction-based model for virtual machine live migration monitoring in a cloud datacenter

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

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

A modelling & Simulation via CloudSim for Live Migration in Virtual Machines

In growing era of cloud computing, one major utility is better and improved performance. For which Cloud Computing is a model which provides various resources available in a pool from where the users can get available the same when required. In the overall process there can be an important issue that is Fault tolerance to provide the high availability for the process which is being required by the user. And simultaneously if any fault occurs then user might not found any server downtime and other kind of delay in getting the response. There are many compute nodes such as Virtual machines further controlled by the Hypervisor. For transferring the load among compute nodes can be done by Cold Migration earlier there was a technique Non-Live migration. But at the same time there were lot many issues and challenges with the same that user might get the server downtime for infinite delay. For overcoming that there is an on-going technique which is known as Hot Migration or Live migration. It allows system administrators to shuffle the load available on an operating system instance to other Guest Operating system instance without any intervention on hosted service. This approach benefits to provide and efficient online system where no downfall in server maintenance. It also cures the load balancing, reconfiguration of data centers. The simulation of above aforesaid concept has been done via Cloudsim a tool for modelling and simulation in Cloud computing environment for live migration of virtual machines without intervention of cloud services. This paper simulated the work and gives the efficient results in comparison to the existing techniques.

A Service Sustainable Live Migration Strategy for Multiple Virtual Machines in Cloud Data Centers

Virtual machine (VM) migration is an indispensable aspect of a virtualized cloud environment. It assists in resource management by dynamically relocating VMs from one physical machine to another. This is an essential aspect especially for big data applications that are prone to variable workloads and often demand relocation of resources in terms of VMs. However, such applications not only experience stochastic workloads but also have stringent requirements on the maximum tolerable latency. To address such issues, VMs are often relocated using live VM migration. VM migration is associated with overheads, hence, in this paper, we propose a modified serial migration strategy to migrate multiple VMs based on the pre-copy live migration technique. We propose to interleave the pre-copy stages in such a way that a balance is achieved between the migration time and downtime overheads. The proposed technique is compared with the state-of-the-art serial, parallel, and improved serial migration strategies. Concerning downtime, the proposed approach performs exceptionally well compared to both serial and improved serial methods. The downtime of the proposed scheme and parallel are comparative for read-intensive applications (low dirtying rates). However, for write-intensive applications (high dirtying rates) the former significantly outperforms the latter. The migration time performance of the proposed scheme is observed to be much better than that of the parallel technique and is slightly higher than those of serial and improved serial techniques.

Application of virtual machine consolidation in cloud computing systems

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

Sova: A Software-Defined Autonomic Framework for Virtual Network Allocations

With the rise of network virtualization, the workloads deployed on data center are dramatically changed to support diverse service-oriented applications, which are in general characterized by the time-bounded service response that in turn puts great burden on the data-center networks. Although there have been numerous techniques proposed to optimize the virtual network allocation in data center, the research on coordinating them in a flexible and effective way to autonomically adapt to the workloads for service time reduction is few and far between. To address these issues, in this article we propose Sova, an autonomic framework that can combine the virtual dynamic SR-IOV (DSR-IOV) and the virtual machine live migration (VLM) for virtual network allocations in data centers. DSR-IOV is a SR-IOV-based virtual network allocation technology, but its operation scope is very limited to a single physical machine, which could lead to the local hotspot issue in the course of computation and communication, likely increasing the service response time. In contrast, VLM is an often-used virtualization technique to optimize global network traffic via VM migration. Sova exploits the software-defined approach to combine these two technologies with reducing the service response time as a goal. To realize the autonomic coordination, the architecture of Sova is designed based on the MAPE-K loop in autonomic computing. With this design, Sova can adaptively optimize the network allocation between different services by coordinating DSR-IOV and VLM in autonomic way, depending on the resource usages of physical servers and the network characteristics of VMs. To this end, Sova needs to monitor the network traffic as well as the workload characteristics in the cluster, whereby the network properties are derived on the fly to direct the coordination between these two technologies. Our experiments show that Sova can exploit the advantages of both techniques to match and even beat the better performance of each individual technology by adapting to the VM workload changes.

МНОГОКРИТЕРИАЛЬНАЯ ОПТИМИЗАЦИЯ РАЗМЕЩЕНИЯ ВИРТУАЛЬНЫХ МАШИН ПО ФИЗИЧЕСКИМ СЕРВЕРАМ В ОБЛАЧНЫХ ЦЕНТРАХ ОБРАБОТКИ ДАННЫХ

The problem of virtual machine placement on physical servers in cloud data centers is considered. The resource management system has a two-level architecture consisting of global and local controllers. Local controllers analyze the state of the physical servers on which they are located and determine possible underloading, overloading, and overheating states based on the forecast for the next observation window. If one of the listed states is detected, the local controller notifies the global controller, which selects the destination servers to host the virtual machines via migration. It is proposed to place virtual machines based on the criteria of minimum remaining unused resources and violation of SLA agreements. A mathematical formulation of the optimization problem is given, which is equivalent to the known main assignment problem in terms of structure, necessary conditions, and the nature of variables. Reducing the assignment problem to a closed transport problem allowed us to solve the problem of hosting virtual machines under many criteria in real time and significantly increase its dimension in comparison with heuristic algorithms, which makes it possible to maintain the quality of modern cloud services in the conditions of rapid growth of physical and virtual resources of data centers. The developed mathematical formulation of the problem and the results of computational experiments can be included in the mathematical software of virtual machine live migration.

Self - Adaptive Load Balancing Using Live Migration of Virtual Machines in Cloud Environment

In this paper, a self-adaptive load balancing technique is proposed using live migration of heterogeneous virtual machines (VM) in a Hyper-V based cloud environment. A cloud supported plugin as a management activity within the infrastructure as a service strategy. It is proposed to assist the load balancing process in such a way so that all hypervisors are almost equally loaded once the overload status gets triggered. In the cloud computing environment, load balancing plays a major role if the large number of events triggered has a high impact on the performance of the system. The efficiency of cloud computing is based on the efficient load balancing having a self-adjustable technique using live migration of VMs across clusters of nodes. The proposed load balancing model is efficient in performance improvement by efficient resource utilization and also it helps to avoid the situation occurrence of server hanging by the cause of server overload within the infrastructure of multiple Microsoft Hyper-V hypervisors environment.

Using Virtual Machine live migration in trace-driven energy-aware simulation of high-throughput computing systems

Virtual Machine (VM) live migration is one of the strategic approaches that can be employed to reduce energy consumption and increase the utilisation of a large computing infrastructure. In this paper, we implement the pre-copy live migration algorithm to provide a test environment for live job migration in a high throughput computing system (HTC). We present details of our extension into the HTC-Sim simulation framework. Furthermore, we demonstrate the performance and energy impact of our responsive live migration approach in high throughput computing (HTC) systems with various migration policies. The proposed policies focus on which computer to select for migration from an HTC resource pool. We evaluate our migration policies through the use of our HTC-Sim simulation framework. Moreover, we compare the results between the policies as well as the system where migration is not considered. We demonstrate that our responsive migration could save approximately 75% of the system wasted energy due to job evictions by user interruptions where migration is not employed as a fault-tolerance mechanism.

Optimizing multi-VM migration by allocating transfer and compression rate using Geometric Programming

Live VM migration has emerged as a core technology for resource management activities and objectives like fault tolerance, reduced energy consumption, load balancing, and system maintenance. Pre-copy migration strategy is frequently used by most of the existing hypervisors for live VM migration. However, it requires a considerable amount of data transfer. In the case of low network bandwidth availability, the migration process gets adversely affected. Thereby degrading the performance of applications running on migrated VMs. Migrating multiple VMs even requires more data to be transferred, which further faces the challenge of sharing the available network bandwidth. This motivates for designing a better strategy for the live migration of multiple VMs. Moreover, to reduce the amount of data that needs to be transferred, memory compression techniques can be utilized efficiently by providing fast and stable VM memory migration without sacrificing any service quality. This paper presents a new model using Geometric Programming that allocates transfer and compression rates to each VM to minimize the total migration time. We have found that: (a). Memory compression, along with pre-copy migration, improves the performance of the live migration of multiple virtual machines by reducing the total migration time and downtime. (b). The compression rate is dynamically allocated according to the available network bandwidth. So, in an adverse environment, when available bandwidth is less, the memory is compressed using slow compression algorithms to optimize the total migration time. In contrast, when more bandwidth is available, it uses a fast compression algorithm to transfer the data quickly. (c). With the proposed approach, more number of VMs can be migrated in parallel with considerably reduced migration time and downtime when compared with existing strategies.

Live Migration Schemes in Data Centers

This paper describes some live virtual machine (VM) migration schemes used in modern hypervisors and provides a comparative analysis of their potential capabilities based on the following criteria: eviction time, total migration time, downtime, total amount of data transmitted during migration, and decrease in performance of a migrating VM.