Dynamic Migration Of Virtual Machines Research Articles

An Energy-Efficient Dynamic Scheduling Method of Deadline-Constrained Workflows in a Cloud Environment

With the rapid development of cloud applications, the computing requests of cloud data centers have increased significantly, consuming a lot of energy, making cloud data centers unsustainable, which is very unfavorable from both the cloud provider’s point of view and the environmental point of view. Therefore, it is crucial to minimize energy consumption and improve resource utilization while ensuring user service quality constraints. In this paper, we propose a hybrid workflow scheduling algorithm (Online Hybrid Dynamic Scheduling, OHDS), which aims to minimize the energy consumption of tasks and maximize service resource utilization while satisfying the sub-deadline and data dependency constraints of workflow tasks. Firstly, the data dependencies between workflow tasks are considered for multi-task merging, and sub-deadline constraints are assigned to workflow tasks based on task priority. Secondly, based on the independent nature of the tasks of different workflows, a hybrid scheduling of multiple workflows is performed to reduce service idle time. Then, the workflow task scheduling priority and its sub-deadlines are dynamically adjusted, and the service status is sensed by the CPU utilization of the service, and the workload on the overloaded/underloaded service is balanced by dynamic migration of virtual machines. Finally, the OHDS method is compared with three existing scheduling methods to verify its better performance in terms of scheduling energy consumption, scheduling success rate and service resource utilization.

A Comprehensive Review of Virtual Machine Migration Techniques in Cloud Computing

As cloud users grow exponentially, virtualization of the cloud data center (CDC) is accomplished by running simultaneous virtual machines (VM) on one cloud server in parallel processing. The growing demand for resources such as communication, storage, and computing can be successfully met by using dynamic virtual machine migration. For single or multiple VM migrations, most resource management techniques are used to maximize the performance and efficiency of the cloud data center. The Qos (service quality) declines proportionally with fast increase in cloud users and associated workload. The powerful live VM migration strategy can only be used to deliver better computing facilities, manage large numbers of cloud users and reduce time and energy on cloud data center. Modern cloud computing system depends primarily on live VM migration. This paper analyzes with their advantages and disadvantages all the latest live VM migration techniques. Their future range research and implementation has been briefly mentioned. Comparisons have been made between pre-copy and post-copy VM Migration techniques through simulation corresponding to CPU uses, memory and network as parameters.This paper reviews various virtual machine migration schemes while, through a comprehensive analysis of existing schemes, examining the critical aspects of virtual machine migration schemes.Existing systems are explored with key goals. In the VM migration domain, some open research issues are also mentioned.

Balanced Cloud Edge Resource Allocation Based on Conflict Conditions

Under a multiscenario environment with frequent bursts of data in the edge cloud, the resource allocation in the edge cloud will affect the stability of its nodes. To address this problem, a balanced virtual resource allocation model based on conflict conditions is proposed. Based on a thorough study of the similarity between task attributes and resources used by the host, the concept of a task backlog is implemented to achieve a preliminary balanced allocation of tasks; thus, a conflict condition based on the remaining resources of the physical and virtual machines is proposed. Further, a matrix of phased conflict coefficients is built to establish a balanced virtual machine allocation model. The results of experiments comparing the performance of the proposed model with that of other existing models indicate that the proposed model can reduce the virtual machine scheduling time by up to 8.33%, save up to 6.25% of host energy consumption, and improve the algorithm efficiency by 20.47% compared with the other algorithms. To avoid the local optimal problem caused by dynamic virtual machine migration, an improved ant colony algorithm is combined with the above model, and concepts of a pheromone volatility factor and suppression factor are implemented to optimize the pheromone measurement function and ensure that the virtual machine migration path is globally optimal. Overall, the model reduces the conflict rate of resources on the physical machine and can maintain stable operation under CPU usage fluctuation, thus realizing a balanced allocation of node resources.

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.

Dynamic migration of virtual machines to reduce energy consumption in a cluster

Virtual machines are widely used to support applications with virtual service in server clusters. Here, a virtual machine can migrate from a host server to a guest server. In this paper, we consider a cluster where virtual machines are dynamically created and dropped depending on the number of processes. We propose a dynamic virtual machine migration (DVMM) algorithm to reduce the total electric energy consumption of servers. If an application issues a process to a cluster, the most energy-efficient host server is first selected and then the process is performed on a virtual machine of the server. Then, a virtual machine migrates from a host server to a guest server so that total electric energy consumption of servers can be reduced. In the evaluation, we show the total electric energy consumption and active time of servers and the average execution time of processes can be reduced in the DVMM algorithm.

Dynamic migration of virtual machines to reduce energy consumption in a cluster

Dynamic migration of virtual machines to reduce energy consumption in a cluster

One‐on‐one contract game–based dynamic virtual machine migration scheme for Mobile Edge Computing

AbstractIn the evolution toward fifth‐generation networks, Mobile Edge Computing (MEC) is an emerging paradigm, conceived to meet the ever‐increasing computational demands of mobile applications. Within the access range of mobile devices, the MEC technique promises the enablement of efficient Mobile Cloud Computing services. In an MEC system, virtual machine (VM) migration is a key issue; VM migration is the process of moving a VM from an edge node to another edge node. To improve service quality and system performance, the VM migration method has a dual focus on the MEC system's computation and communication resources. In this study, we formulate the VM migration problem as a one‐on‐one contract game model and develop a learning‐based price control mechanism to effectively handle the MEC's resource. By using the game methodology and learning process, our approach is able to capture the dynamics of MEC systems, and it interacts continually with an unknown system environment. Finally, extensive simulation results are provided to demonstrate the capability of the proposed approach in achieving, with respect to existing MEC schemes, both higher resource utilization and system throughput, as well as reduced service drop ratio and reduced service delay.

BGM-BLA: A New Algorithm for Dynamic Migration of Virtual Machines in Cloud Computing

Cloud computing is getting more prevalent and finding a way to reduce the cost of cloud computing platform through the migration of virtual machines (VM) is a concerned issue. In this paper, the problem of dynamic migration of VMs (DM-VM) in the cloud computing platform (or simply the cloud) is investigated. A triple-objective optimization model for DM-VM is established, which takes energy consumption, communication between VMs, and migration cost into account under the situation that the platform works normally. The DM-VM problem is divided into two parts: (i) forming VMs into groups, and (ii) determining the best way to place the groups into certain physical nodes. A binary graph matching-based bucket-code learning algorithm (BGM-BLA) is designed for solving the DM-VM problem. In BGM-BLA, bucket-coding and learning is employed for finding the optimal solutions, and binary graph matching is used for evaluating the candidate solutions. The computational results demonstrate that the proposed BGM-BLA algorithm performs relatively well in terms of the Pareto sets obtained and computational time in comparison with two optimization algorithms, i.e., Non-dominated Sorting Genetic Algorithm (NSGA-II) and binary graph matching-based common-coding algorithm.

Optimized Energy Efficient Virtual Machine Placement Algorithm and Techniques for Cloud Data Centers

Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that's why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. Hence there is need for green computing and for energy efficient management of cloud data center resources besides meeting the QoS constraints. Within a data center, maximum energy is consumed by cooling systems and ICT Infrastructure, particularly, the servers. So it is of utmost importance to optimize energy utilization in datac enter servers. In order to achieve this, we can leverage the power of virtualization which is intrinsic in Cloud Computing. Virtualization opens the doors for VM consolidation by allowing dynamic migration of virtual machines across physical machines. In this study, the problem of VM consolidation has been formulated as a mathematical optimization problem.

Reduction of Power Consumption in Cloud Data Centers via Dynamic Migration of Virtual Machines

Reduction of Power Consumption in Cloud Data Centers via Dynamic Migration of Virtual Machines

Research on Trigger Strategy of Virtual Machine Dynamic Migration in Cloud Computing

According to the trigger strategy of virtual machine dynamic migration based on features closed in the process of dynamic migration of virtual machines in the cloud computing, this paper puts forward a double threshold trigger strategy using timing prediction based on historical data (DTS Algorithms). Then simulation on the CloudSim platform, and analyze the results of the experiment. Experimental results showed that in the system virtual machine migration using DTS algorithm can reduce the number of migration and the energy consumption during the migration process.

Dynamic VM migration

Dynamic migration of virtual machines (VMs) across physical servers has the potential to increase the utilization of the servers and hence drive down the data center costs. However, IT practitioners are leery of using this capability for increasing resource utilization due to concerns about the impact of such migrations on the performance of the applications, particularly the response times seen by the users of the applications. The relative newness to the industry of many of the tools used to automate VM migrations for resource utilization; data from researchers; as well as the recommendations from some analysts justify such caution and warrant quantifying the risks as well as potential rewards before deciding how aggressively this capability should be adopted. This paper will discuss the requirements for a benchmark to be used for such quantification. We will also discuss adaptations to SPECvirt sc2010, originally developed as a single server benchmark, to meet these requirements. We will also present risk-reward quantifications obtained using this benchmark for a simple case and the broader use of the benchmark for other cases.