Live Migration Of Virtual Machines Research Articles

A prediction model for improving virtual machine live migration performance in cloud computing using artificial intelligence techniques

Over recent years, virtualization has worked as the powerhouse of the data centers. To positively influence datacenter utilization, power consumption, and management, live migration presents a technique which must be employed. Live migration refers to the method whereby an online virtual machine or an application is moved from one physical server to another without shutting down the client or the program. However, a migration’s qualities, which define service quality, have to be evaluated prior to a migration. In this paper, we propose a novel approach using predictive machine learning and deep learning models to forecast key metrics such as total migration time, downtime, and total data transferred for two migration types: pre-copy migration and post-copy migration. It builds on methods from regression machine learning and deep learning, and hyperparameter optimization, and feature engineering to push past reported solutions. The novelty of this work is the development of the hybrid model that includes the positive aspects from both of the machine learning and the deep learning with an emphasis on the increased accuracy of the model’s predictions. Concretely, the proposed deep learning framework based on GRU provided outstanding performance with 99.6% in total migration time where the transferred data GRU achieved 99.9% Moreover, in Down time target GRU reached 98.3%. This outperforms the other machine learning model and prior studies to become the best model for live migration prediction.

Correction to: Optimizing pre‑copy live virtual machine migration in cloud computing using machine learning‑based prediction model

Correction to: Optimizing pre‑copy live virtual machine migration in cloud computing using machine learning‑based prediction model

A secure VM live migration technique in a cloud computing environment using blowfish and blockchain technology

A secure VM live migration technique in a cloud computing environment using blowfish and blockchain technology

An intelligent decision system for virtual machine migration based on specific Q-learning

SummaryDue to the convenience of virtualization, the live migration of virtual machines is widely used to fulfill optimization objectives in cloud/edge computing. However, live migration may lead to side effects and performance degradation when migration is overused or an unreasonable migration process is carried out. One pressing challenge is how to capture the best opportunity for virtual machine migration. Leveraging rough sets and AI, this paper provides an innovative strategy based on Q-learning that is designed for migration decisions. The highlight of our strategy is the harmonious mechanism for applying rough sets and Q-learning. For the ABDS (adaptive boundary decision system) strategy in this paper, the exploration space of Q learning is confined by the boundary region of rough sets, while the thresholds of the boundary region can be dynamically adjusted by the reaction results from the computing cluster. The structure and mechanism of the ABDS strategy are described in this paper. The corresponding experiments show a firm advantage for the cooperation of rough sets and reinforcement learning algorithms. Considering both the energy consumption and application performance, the ABDS strategy in this paper outperforms the benchmark strategies in comprehensive performance.

Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model

One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) is an option to address service unavailability by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration is a common LVM approach used in cloud systems, but it faces challenges due to the high rate of frequently updated memory pages known as dirty pages. Transferring these dirty pages during pre-copy migration prolongs the overall migration time. If there are large numbers of remaining memory pages after a predefined iteration of page transfer, the stop-and-copy phase is initiated, which significantly increases downtime and negatively impacts service availability. To mitigate this issue, we introduce a prediction-based approach that optimizes the migration process by dynamically halting the iteration phase when the predicted downtime falls below a predefined threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QEMU technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and default migration approach reveals a remarkable improvement, with an average 64.91% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. These findings underscore the practical advantages of our method in reducing service disruptions during live virtual machine migration in cloud systems.

EnCloud: Aspect‐oriented trusted service migration on SGX‐enabled cloud VM

AbstractThis paper presents enCloud, a new aspect‐oriented trusted service migration with SGX‐enabled cloud VM. Addressing the challenge of reconciling end‐to‐end security with VM migration, enCloud incorporates two key aspects: (1) end‐to‐end security for enclave context migration, and (2) VM abstraction for conventional VM context migration. This paper provides a practical guideline with applicable APIs for trusted service migration. In a case study, enCloud demonstrates effective trusted DB service migration on a cloud VM, achieving end‐to‐end security with minimal trust boundaries. The framework supports pre‐copy live VM migration to minimize service downtime. This paper contributes a concise and practical solution in the form of the enCloud framework for secure service migration.

Reinforcement Learning-Driven Decision-Making for Live Virtual Machine Migration in Fog Computing

Virtualization is an essential mechanism in fog computing that enables elasticity and isolation, which in turn helps achieve resource efficiency. To bring high flexibility in a fog environment, migration of virtual machines from one node to another is required. This can be achieved by live virtual machine migration to reduce downtime and delays. Multiple existing studies have discussed live virtual machine migration in a fog environment. However, these studies have some limitations, such as pre-migrating the virtual machines based on mobility prediction only or based on the load only, which causes an issue of late and early handover. Due to the dynamic nature of fog environments, VM migration decisions require consideration of multiple factors. Hence, there is a need to develop a system that considers multiple factors to decide to migrate a virtual machine or not to solve the issue of early and late handover. This study proposes a novel approach to live virtual machine migration that applies reinforcement learning for decision-making. Experiments show that the proposed approach significantly reduces the latency of time-critical applications. The proposed system, outperforms the existing systems in terms of total average reward. The system outperformed the mobility-only-based system by 97% when tested with two fog nodes and by 80% when tested with sixteen fog nodes in terms of average reward. Further, the proposed system outperforms the load-based system by 50% and 75% when the environment consists of two fog nodes and sixteen fog nodes, respectively. This proved that considering multiple factors in deciding virtual machine migration in a fog environment can be effectively applied in time-critical applications to reduce latency.

A method for assessing the characteristics of the virtual machine migration process, taking into account the type of migrations and applications

Dynamic resource allocation in cloud computing is a critical aspect. It is necessary for computing resources scaling to maintain application performance, reduce costs and ensure the reliability of IT systems. Dynamic resource allocation is possible using open virtual machines (VM), but live migration is a relatively expensive and resource-intensive operation. A number of VM migration algorithms have been proposed, each with different performance characteristics depending on the state of the host system, network, and the VM workload. Recently, a large number of works have emerged that have developed models for assessing the effectiveness of of VM migration, but many of them do not provide a standard of satisfactory prediction accuracy and are built for a single migration algorithm, which limits the use of data models. This work uses a statistical approach to estimate the total migration time and VM downtime based on the approximation of probability densities by Gram-Charlier and Laguerre Series. The proposed method, in comparison with the known ones, allows one to answer the question of what is the probability of total migration time and VM downtime for a given migration type and VM application. The analysis of total migration time and VM downtime is based on the Virtual Machine Live Migration Dataset includes more than 40 000 virtual machine migration records with five different live migration algorithms: post-copy migration, pre-copy migration and its modifications: CPU throttling delta compression, and data compression. The dataset includes approximately 8000 migration records of each migration algorithm with 9 types of workloads. Analysis of the results allows us to conclude that the type of application significantly influences both the shape of the empirical distribution (histograms) of total migration time and its characteristics, therefore it is advisable to obtain an analytical expression for the distribution law of the total migration time and VM downtime taking into account these circumstances. The results of the experiments shows that this method does not depend on the migration algorithm and the type of working application. The use of the Laguerre series can be recommended as giving more reliable results compared to Gram-Charlier series. A method for estimating total migration time and VM downtime can be integrated into a management system to select the best monitoring window for servers and evaluate service-level agreement terms.

Enhancing Security in Cloud-Based VM Migration: A Trust-Centric Hybrid Optimization Approach

Virtual Machine (VM) migration in cloud computing is essential to accomplishing various resource management goals, such as load balancing, power management, and resource sharing. Ensuring Quality of Service (QoS) is crucial while migrating VMs, which means that VMs must run continuously during the procedure. The dynamic nature of resource requirements in cloud computing, driven by “service-on-demand,” poses security risks, even while live VM migration mechanisms help to maintain VM availability. This paper addresses changing resource requirements and improves overall system security by introducing a novel method for safe live VM migration. In this paper, a robust optimization approach has been proposed for live VM migration named the Gorilla-based Shuffled Shepherd Optimization Approach (GBSSOA). The proposed approach maximizes the number of fitness targets, such as migration time, QoS parameters, and job runtime. The method takes into account trust values in addition to the previously described performance measures, with a focus on security. Key performance indicators QoS as workload, migration time, trust, and GBSSOA-based secure live VM migration are taken into account during the evaluation. The obtained values of 0.141, 0.654, 342.254ms, and 0.569, respectively, show noteworthy accomplishments in the results. These results highlight how well the strategy developed may simultaneously improve performance metrics and strengthen the security of live VM migration in dynamic cloud computing environments.

Enhancing Security in Cloud-Based VM Migration: A Trust-Centric Hybrid Optimization Approach

Virtual Machine (VM) migration in cloud computing is essential to accomplishing various resource management goals, such as load balancing, power management, and resource sharing. Ensuring Quality of Service (QoS) is crucial while migrating Virtual Machines (VMs), which means that VMs must run continuously during the procedure. The dynamic nature of resource requirements in cloud computing, driven by "service-on-demand," poses security risks, even while live VM migration mechanisms help to maintain VM availability. This paper addresses changing resource requirements and improves overall system security by introducing a novel method for safe live virtual machine migration. In this paper, a robust optimization approach has been proposed for live virtual machine migration named the Gorilla-based Shuffled Shepherd Optimization Approach (GBSSOA). The proposed approach maximizes the number of fitness targets, such as migration time, QoS parameters, and job runtime. The method takes into account trust values in addition to the previously described performance measures, with a focus on security. Key performance indicators QoS as workload, migration time, trust, and GBSSOA-based secure live virtual machine migration are taken into account during the evaluation. The obtained values of 0.141, 0.654, 342.254 ms, and 0.569, respectively, show noteworthy accomplishments in the results. These results highlight how well the strategy developed may simultaneously improve performance metrics and strengthen the security of live virtual machine migration in dynamic cloud computing environments.

Minimizing Virtual Machine Live Migration Latency for Proactive Fault Tolerance Using an ILP Model With Hybrid Genetic and Simulated Annealing Algorithms

Minimizing Virtual Machine Live Migration Latency for Proactive Fault Tolerance Using an ILP Model With Hybrid Genetic and Simulated Annealing 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.

DALMIG: Matching-Based Data Center Allocation and Dual Live VM Migration in Cluster-Based Federated Cloud

The modern era has been ruled by federated cloud extensively owing to the upward popularity of real-time applications. However, balancing the load among the different service providers in the federation is a challenging task. This is because of the enlarged load of each service provider and the demand for Service Level Agreement (SLA) requirements. Live VM migration is an effective mechanism to balance the load among the different Cloud Service Providers (CSP). Yet, none of the works has focused on both inter- and intra- CSP-based live VM migration in the federated cloud. Besides, they utilized traditional techniques (Pre and Post-copy) to perform live VM migration. This increases migration and downtime in a federated cloud environment. Motivated by this, we propose matching-based data center allocation and Duple live VM migration in clustered federated cloud environment (DALMIG). The proposed DALMIG approach tackles the downsides faced in the federated cloud as yet. In this regard, three processes are contributed that are Matching-based Data Center Allocation, Two-Fold Clustering, and Duple live VM migration. Initially, brokers allocate apt data center to each incoming task using Auction-based Bipartite One to N (A-BON) matching algorithm. And then, we execute two-fold clustering process: Here, the data center is clustered using the Kernel Neutrosophic C-Means (kernel NCM) algorithm. And, we cluster VMs in each data center using the Credal C-Means (CCM) algorithm. To maintain the load of each CSP, we maintain two entities, such as a load monitor and a migrator. Here, the load monitor estimates a load of each data center and the migrator performs a live VM migration process. Here, the live VM migration process is performed using the Hybrid Copy technique. We execute both intra- and inter-CSP live VM migration. Here, intra-live VM migration is performed using the State Action Reward State Action (SARSA) algorithm. Inter-CSP live VM migration is accomplished using the Black Widow Optimization (BWO) algorithm. We show our DALMIG performance in the federation using the Cloudsim tool. The simulation results acquired from Cloudsim are auspicious in the context of metrics including latency, response time, number of VM migrations, Migration time, load fairness, and communication overhead.

Secure Virtual Machine Live Migration using Advanced Metric Encryption

Abstract: Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is expected to be with the aim of having migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and identified research gaps to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated.

Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.

A machine learning-based optimization approach for pre-copy live virtual machine migration

Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infrastructure, load balancing of services, defense against cyber attacks, power management, proactive fault tolerance, or resource usage. The unavailability of cloud services impacts negatively on the business model of cloud providers. One solution to tackle the service unavailability is Live Virtual Machine Migration (LVM), that is, moving virtual machines (VMs) from the source host machine to the destination host without disrupting the running application. Pre-copy memory migration is a common LVM approach used in most networked systems such as the cloud. The main difficulty with this approach is the high rate of frequently updating memory pages, referred to as "dirty pages. Transferring these updated or dirty pages during the pre-copy migration approach prolongs the total migration time. After a predefined iteration, the pre-copy approach enters the stop-and-copy phase and transfers the remaining memory pages. If the remaining pages are huge, the downtime or service unavailability will be very high -resulting in a negative impact on the availability of the running services. To minimize such service downtime, it is critical to find an optimal time to migrate a virtual machine in the pre-copy approach. To address the issue, this paper proposes a machine learning-based method to optimize pre-copy migration. It has mainly three stages (i) Feature selection (ii) Model generation and (iii) Application of the proposed model in pre-copy migration. The experiment results show that our proposed model outperforms other machine learning models in terms of prediction accuracy and it significantly reduces downtime or service unavailability during the migration process.

Pre-copy live virtual machine migration techniques in cloud computing using HDWHM algorithm

Live Virtual Machine (VM) migration is a legacy of server virtualization in cloud computing. One of the difficulties in a Virtual Machine (VM) Environment is load balancing, and the pre-copy migration strategy is both time-consuming and expensive. In this paper, we propose an efficient VM migration strategy to compute the parameters for Linear Adam Algorithm overload detection and Interquartile Range (IQR) under-loaded identification, the Central Processing Unit (CPU) utilization is sufficient. VMs require constant network traffic, storage, CPU, and memory demands. This study recommends using the novel Hamming Distance-Based Weighted Harmonic Mean (HDWHM) strategy for pre-copy live VM migration. We tested the suggested strategy using datasets from CloudSim-3.0.3 on a real PlanetLab. Simulations show that the suggested approach decreases host downtimes, energy consumption, VM migrations, and SLA degradation.

Memory sharing for handling memory overload on physical machines in cloud data centers

Over-committing computing resources is a widely adopted strategy for increased cluster utilization in Infrastructure as a Service (IaaS) cloud data centers. A potential consequence of over-committing computing resources is memory overload of physical machines (PMs). Memory overload occurs if memory usage exceeds a defined alarm threshold, exposing running computation tasks at a risk of being terminated by the operating system. A prevailing measure to handle memory overload of a PM is live migration of virtual machines (VMs). However, this not only consumes network bandwidth, CPU, and other resources, but also compels a temporary unavailability of the VMs being migrated. To handle memory overload, we present a memory sharing system in this paper for PMs in cloud data centers. With memory sharing, a PM automatically borrows memory from a remote PM when necessary, and releases the borrowed memory when memory overload disappears. This is implemented through swapping inactive memory pages to remote memory resource. Experimental studies conducted on InfiniBand-networked PMs show that the memory sharing system is fully functional. The measured throughput and latency are around 929 Mbps and 1.3 mus, respectively, on average for remote memory access. They are similar to those from accessing a local-volatile memory express solid-state drive, and thus are promising in real applications.

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers’ (CDC) proficiency as it leads to better resource usage. The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available. Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data. To overcome this issue, a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory (CDB-LSTM) model is proposed. The CDB-LSTM adopts Helly property of Hypergraph and Savitzky–Golay (SG) filter to select informative samples and exclude noisy inference & outliers. The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computational complexity during live VM migration. Further, the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration. A Hypergraph based CDB-LSTM was validated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

Live Virtual Machine Migration in Fog Computing: State of the Art

Fog computing is an emerging paradigm which extends the functionality of the cloud near to the end users. Its introduction helped in running different real-time applications where latency is a critical factor. This paradigm is motivated by the fast growth of Internet of Things (IoT) applications in different fields. By running Virtual Machines (VMs) on fog devices, different users will be able to offload their computational tasks to fog devices to get them done in a smooth, transparent, and faster manner. Nevertheless, the performance of real-time applications might suffer if no proper live virtual machine migration mechanism is adopted. Live VM migration aims to move the running VM from one physical fog node to another with minimal or zero downtime due to mobility issues. Many efforts have been made in this field to solve the challenges facing live VM migration in fog computing. However, there are remaining issues that require solutions and improvements. In this paper, the following presents the research outcomes: An extensive survey of existing literature on live VM migration mechanisms in fog computing. Also, a new novel classification approach for categorizing live VM migration mechanisms based on conventional and Artificial Intelligence (AI) approaches to address live VM migration challenges is presented. Moreover, an identification of research gaps and in the existing literature and highlighting the areas where further investigation is required is done and finally a conclusion with a discussion of potential future research directions is drawn