Virtual Machine Migration Strategy Research Articles

Virtual Machine Migration Strategy Based on Markov Decision and Greedy Algorithm in Edge Computing Environment

In view of the problem of long migration time and low availability of VM (virtual machine) migration in MD (mobile devices) in the MEC (mobile edge computing) environment, this paper designs a MD2N (Mobile Device to Network) model. This model no longer uploads requests to the cloud center, but centers on the edge server, scheduling and selecting migration services through the communication between the servers. And, a virtual machine migration strategy based on Markov decision and greedy algorithm is proposed. Model the edge server based on its geographic location, imitating the traditional cellular network method, and then the improved greedy algorithm is used to dynamically select the best edge device to migrate VM. Simulation and comparison experiments show that the proposed scheme has small migration fluctuations, stable migration status, short migration time, low average time delay, and high virtual machine migration availability after migration.

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.

Research on energy‐saving virtual machine migration algorithm for green data center

AbstractThe cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service.

Cloud Host Selection using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation

A significant portion of the energy consumption in cloud data centres can be attributed to the inefficient utilization of available resources due to the lack of dynamic resource allocation techniques such as virtual machine migration and workload consolidation strategies to better optimize the utilization of resources. We present a new method for optimizing cloud data centre management by combining virtual machine migration with workload consolidation. Our proposed Energy Efficient Particle Swarm Optimization (EE-PSO) algorithm to improve resource utilization and reduce energy consumption. We carried out experimental evaluations with the Container CloudSim toolkit to demonstrate the effectiveness of the proposed EE-PSO algorithm in terms of energy consumption, quality of service guarantees, the number of newly created VMs, and container migrations.

Hybrid Optimization Algorithm for VM Migration in Cloud Computing

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

A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks

With the rapid development of Internet of Things technology, a large amount of user information needs to be uploaded to the cloud server for computing and storage. Side-channel attacks steal the private information of other virtual machines by coresident virtual machines to bring huge security threats to edge computing. Virtual machine migration technology is currently the main way to defend against side-channel attacks. VM migration can effectively prevent attackers from realizing coresident virtual machines, thereby ensuring data security and privacy protection of edge computing based on the Internet of Things. This paper considers the relevance between application services and proposes a VM migration strategy based on service correlation. This strategy defines service relevance factors to quantify the degree of service relevance, build VM migration groups through service relevance factors, and effectively reduce communication overhead between servers during migration, design and implement the VM memory migration based on the post-copy method, effectively reduce the occurrence of page fault interruption, and improve the efficiency of VM migration.

Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning

Mobile edge computing is a new computing model, which pushes cloud computing power from centralized cloud to network edge. However, with the sinking of computing power, user mobility brings new challenges: since it is usually unstable, services should be dynamically migrated between multiple edge servers to maintain service performance, that is, user-perceived latency. Considering that Mobile Edge Computing is a highly distributed computing environment and it is difficult to synchronize information between servers, in order to ensure the real-time performance of the migration strategy, a virtual machine migration strategy based on Multi-Agent Deep Reinforcement Learning is proposed in this paper. The method of centralized training and distributed execution is adopted, that is, the transfer action is guided by the global information during training, and only the local observation information is needed to obtain the transfer action. Compared with the centralized control method, the proposed method alleviates communication bottleneck. Compared with other distributed control methods, this method only needs local information, does not need communication between servers, and speeds up the perception of the current environment. Migration strategies can be generated faster. Simulation results show that the proposed strategy is better than the contrast strategy in terms of convergence and energy consumption.

A Combined Traffic and Workload-aware Optimized Virtual Machine Migration in Cloud Computing

In cloud computing paradigm, Virtual Machine (VM) migration strategies play a vital role in reducing the Energy Consumption (EC) and balancing the workloads in the cloud servers. From this viewpoint, an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction and Multipath Traffic Routing (OH-BAC-FUP-MTR) strategy was designed to accomplish effective load-balancing and minimize the chance of traffic congestion during VM migration in data centres. But, the tradeoffs among workload efficiency and power-gain in heterogeneous cloud servers were not analyzed. Hence in this article, a VM consolidation strategy is proposed with OH-BAC-FUP-MTR to switch the idle Physical Machines (PMs) into hibernation mode, resulting in very less EC. In this strategy, the VM migration is targeted at consolidating the VMs depending on the workload to the smaller number of PMs for reducing the power use and encouraging green computing. Initially, the PMs are split into various groups depending on their workload levels and then a new Merge-and-Split-based Coalitional Game-theoretic (MSCG) method is applied to select associates from these groups to create efficient coalitions. After that, OH-BAC-FUP-MTR is performed amongst the coalition associate for maximizing the reward of each coalition and PMs are maintained to operate in the maximum power-efficient condition. Finally, the investigational outcomes exhibit the OH-BAC-FUP-MTR-MSCG achieves a mean EC of 60.63KWh which is 28.87% less than all other classical VM migration strategies.

An energy-aware virtual machine migration strategy based on three-way decisions

Virtual machine migration (VMM) is a crucial way to ensure load balancing and save energy consumption of cloud hosts. In most previous studies, they apply the same virtual machine migration strategy for overloaded hosts, which ignore the possible communication between the migrated VM, and other factors. Thus, they are prone to incur excessive network overhead. This paper proposes a virtual machine migration strategy based on the three-way decision (VMM-3WD) to save cloud hosts’ energy consumption while considering the network correlation between virtual machines. The strategy first is to classify hosts into overloaded hosts, regular load hosts, and under-loaded hosts based on their load situation. Then, different migration strategies are targeted developed for these three types of cloud hosts. Specifically, the approach migrates the VMs in under-loaded hosts to regular load hosts. And then, the approach further develops two thresholds to divides overloaded hosts into massively overloaded hosts, moderately overloaded, and lightly overloaded hosts. The migration decision of VMs in all stages pursuing the goal of reducing the energy consumption of the network during the migration process. The experimental results show that the proposed algorithm can reduce the energy consumption generated by virtual machine migration and data center (DC) energy consumption while meeting the SLA.

Mitigation Of Virtual Machine (VM) Using Cloud Computing

Cloud computing enables users to move their calculations to a distributed environment, where additional resources may be used to finish execution more rapidly. Virtualization refers to the ability to run numerous virtual machines (VM) on a single physical system. VM switches one physical computer to another during the migration process. Offline migration stops the operation until the virtual machine can continue on the destination system, but live migration continues uninterrupted. Live migration is one in which the VM seems to be responding all of the time from the perspective of the clients. A virtual machine (VM) is a computer system that is emulated. Virtual machine migration is a helpful tool in cloud computing for moving Operating System instances across different physical machines. It is used to balance loads, manage faults, perform low-level system maintenance, and minimize energy usage. For VM migration, several approaches and parameters are available. The different virtual machine migration strategies are discussed in this study.

Optimizing the Topology and Energy-Aware VM Migration in Cloud Computing

The advancements in virtual machine migration (VMM) have been trending due to its effective load balancing features in cloud infrastructure. Previously, data centers were used for handling VMs organized in racks. These racks are arranged in a spanning tree topology with a high bandwidth. Thus, the cost for moving the data between servers is highest when the racks are far from each other. This work addresses this issue and proposed VMM strategy based on self-adaptive D-Crow algorithm (S-DCrow) that incorporates adaptive constants in Dragonfly-based Crow (D-Crow) optimization algorithm based on the proposed topology model. The proposed S-DCrow describes a migrating model, which is based on topology, energy consumption, load, and migration cost. Here, the network is organized in a spanning tree topology and is adapted by proposed S-DCrow for optimal VMM. The performance of the proposed S-DCrow shows superior performance in terms of load, energy consumption, and migration cost with the values of 0.1417, 0.1009, and 0.1220, respectively.

ChicWhale optimization algorithm for the VM migration in cloud computing platform

Nowadays, Virtual Machine (VM) migration becomes very popular in the cloud computing platform. Various VM migration based mechanisms are designed for optimal VM placement but remain a challenge due to improper energy consumption in the cloud model. This paper proposes an approach for VM migration in the cloud using an optimization algorithm, Chicken-Whale optimization algorithm (ChicWhale), which is developed by integrating the Whale optimization algorithm in Chicken swarm optimization. In the developed approach, a local migration agent is utilized for monitoring the memory and resources utilization in the cloud continuously, and the VM is migrated using the service provider based on the requirement of the VMs to complete a task assigned. At first, the cloud system is designed, and then the proposed ChicWhale is employed by moving the VMs optimally, and the fitness function for best VM migration is carried out by considering several parameters, like load, migration cost, resource availability, and energy. The performance of the VM migration strategy based on ChicWhale is evaluated in terms of energy consumption, resource availability, migration cost, and load. The proposed ChicWhale method achieves the maximal resource availability of 0.989, minimal migration cost of 0.0564, the minimal energy consumption of 0.481, and the minimal load of 0.0001.

Performance degradation assessment and VM placement policy in cloud

In virtualized servers, with live migration technique pages are copied from one physical machine to another while the virtual machine (VM) is running. The dynamic migration of virtual machines encumbers the data center which in turn reduces the performance of applications running on that particular physical machine. A considerable number of studies have been carried out in the area of performance evaluation during live VM migration. However, all the aspects related to the migration process have not been examined for the performance assessment. In this paper, we propose a novel approach to evaluate the performance during migration process in different types of coupled machine environment. It is presented here that the state of art VM migration technology requires further improvement in realizing effective migration by monitoring comprehensive performance value. We introduced the parameter, θ, to compare performance value which can be used for controlling and halting unsuccessful migration and save significant amount of time in migration operation. Our model is capable of analyzing real time scenario of cloud performance assessment targeting VM migration strategies. It also offers the possibility of further expanding to universal models for analyzing the performance variations that occurs as a result of VM migration.

Multi‐objective constraint and hybrid optimisation‐based VM migration in a community cloud

The growing demand for the cloud community market towards attracting and sustaining the incoming and the available cloud users is addressed actively to meet the competitive environment. There is a good scope for improving the provider capabilities in the cloud in order to satisfy the users with attractive benefits. The study introduces an effective virtual machine (VM) migration strategy using an optimisation algorithm in such a way to facilitate the user selection of the providers based on their budgetary requirements in running their own platforms. The constraints associated with the selection of the provider include cost, revenue, and resource, which are altogether confined as an elective factor. The optimisation algorithm employed for the VM migration is referred to as Taylor series-based salp swarm algorithm (Taylor-SSA) that is the integration of the Taylor series with SSA. The evaluation of the method is progressed using three setups by varying the number of providers and users. The cost, the revenue, and the resource of the proposed method are analysed and concluded that the proposed method acquired a minimal cost, maximal resource gain and revenue.

High-efficient energy saving processing of big data of communication under mobile cloud computing

From 21st century, it is hard for traditional storage and algorithm to provide service with high quality because of big data of communication which grows rapidly. Thus, cloud computing technology with relatively low cost of hardware facilities is created. However, to guarantee the quality of service in the situation of the rapid growth of data volume, the energy consumption cost of cloud computing begins to exceed the hardware cost. In order to solve the problems mentioned above, this study briefly introduced the virtual machine and its energy consumption model in the mobile cloud environment, introduced the basic principle of the virtual machine migration strategy based on the artificial bee colony algorithm and then simulated the performance of processing strategy to big data of communication based on artificial bee colony algorithm in mobile cloud computing environment by CloudSim3.0 software, which was compared with the performance of two algorithms, resource management (RM) and genetic algorithm (GA). The results showed that the power consumption of the migration strategy based on the artificial bee colony algorithm was lower than the other two strategies, and there were fewer failed virtual machines under the same number of requests, which meant that the service quality was higher.

Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

Nowadays, virtual machine migration (VMM) is a trending research since it helps in balancing the load of the Cloud effectively. Several VMM-based strategies defined in the literature have considered various metrics, such as load, energy, and migration cost for balancing the load of the model. This paper introduces a novel VMM strategy by considering the load of the Cloud network. Two important aspects of the proposed scheme are the load prediction through the support vector regression (SVR) and the optimal VM placement through the proposed dragonfly-based crow (D-Crow) optimization algorithm. The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm (CSA) into dragonfly algorithm (DA). Also, the proposed VMM strategy defines a load balancing model based on the energy consumption, load, and the migration cost to achieve the energy-aware VMM. The simulation of the proposed VMM strategy is done based on the metrics such as load, energy consumption, and the migration cost. From the results, it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%, 10.0368%, and 11.0639% for the load, energy consumption, and migration cost, respectively.

A Versioning Approach to VM Live Migration

In the context of virtual machines live migration, two strategies called “pre-copy” and “post-copy” have already been presented; but each of these strategies works well only in some circumstances. In this paper, we have a brief presentation of QAVNS and then introduce a new approach which is based on the concept of object, assigning QAVNS-scheme-revision number, and observing the changes. In this approach, the total virtual machine memory is considered as a QAVNS informational object that is constantly changing. In this regard, we have defined some criteria and presented an algorithm by which the hypervisor can detect the current behavior of the virtual machine, and automatically select the virtual machine migration strategy from the two pre-copy and post-copy options. We evaluated the implementation & simulation platforms considering the state of the art available technologies, mostly CloudSim, SimGrid and DartCSim+. Formal analysis shows that applying the proposed scheme and the proposed algorithm can significantly improve the live migration process of virtual machines.

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

SummaryThe scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.

DEGSA-VMM

PurposeThis paper aims to develop the Dragonfly-based exponential gravitational search algorithm to VMM strategy for effective load balancing in cloud computing. Due to widespread growth of cloud users, load balancing is the essential criterion to deal with the overload and underload problems of the physical servers. DEGSA-VMM is introduced, which calculates the optimized position to perform the virtual machine migration (VMM).Design/methodology/approachThis paper presents an algorithm Dragonfly-based exponential gravitational search algorithm (DEGSA) that is based on the VMM strategy to migrate the virtual machines of the overloaded physical machine to the other physical machine keeping in mind the energy, migration cost, load and quality of service (QoS) constraints. For effective migration, a fitness function is provided, which selects the best fit that possess minimum energy, cost, load and maximum QoS contributing toward the maximum energy utilization.FindingsFor the performance analysis, the experimentation is performed with three setups, with Setup 1 composed of three physical machines with 12 virtual machines, Setup 2 composed of five physical machines and 19 virtual machines and Setup 3 composed of ten physical machines and 28 virtual machines. The performance parameters, namely, QoS, migration cost, load and energy, of the proposed work are compared over the other existing works. The proposed algorithm obtained maximum resource utilization with a good QoS at a rate of 0.19, and minimal migration cost at a rate of 0.015, and minimal energy at a rate of 0.26 with a minimal load at a rate of 0.1551, whereas with the existing methods like ant colony optimization (ACO), gravitational search algorithm (GSA) and exponential gravitational search algorithm, the values of QoS, load, migration cost and energy are 0.16, 0.1863, 0.023 and 0.29; 0.16, 0.1863, 0.023 and 0.28 and 0.18, 0.1657, 0.016 and 0.27, respectively.Originality/valueThis paper presents an algorithm named DEGSA based on VMM strategy to determine the optimum position to perform the VMM to achieve a better load balancing.

RETRACTED ARTICLE: Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm

The complexity, scale and dynamic of data source in the human-centric computing bring great challenges to maintainers. It is problem to be solved that how to reduce manual intervention in large scale human-centric computing, such as cloud computing resource management so that system can automatically manage according to configuration strategies. To address the problem, a resource management framework based on resource prediction and multi-objective optimization genetic algorithm resource allocation (RPMGA-RMF) was proposed. It searches for optimal load cluster as training sample based on load similarity. The neural network (NN) algorithm was used to predict resource load. Meanwhile, the model also built virtual machine migration request in accordance with obtained predicted load value. The multi-objective genetic algorithm (GA) based on hybrid group encoding algorithm was introduced for virtual machine (VM) resource management, so as to provide optimal VM migration strategy, thus achieving adaptive optimization configuration management of resource. Experimental resource based on CloudSim platform shows that the RPMGA-RMF can decrease VM migration times while reduce physical node simultaneously. The system energy consumption can be reduced and load balancing can be achieved either.