Optimizing the Migration of Virtual Machines in Cloud Data Centers

Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.

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Resource and Energy Efficient Virtual Machine Migration in Cloud Data Centers

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Resource Management and Efficiency in Cloud Computing Environments ◽

10.4018/978-1-5225-1721-4.ch009 ◽

2017 ◽

pp. 210-238

Author(s):

Subrat Kumar Dhal ◽

Harshit Verma ◽

Sourav Kanti Addya

Keyword(s):

Cloud Computing ◽

Data Centers ◽

Cloud Model ◽

Service Level Agreement ◽

Optimal Solution ◽

Service Level ◽

Virtual Machine Migration ◽

Cloud Data ◽

Cloud Data Centers ◽

Selection Algorithms

Cloud computing service has been on the rise over the past few decades, which has led to an increase in the number of data centers, thus consuming more amount of energy for their operation. Moreover, the energy consumption in the cloud is proportional to the resource utilization. Thus consolidation schemes for the cloud model need to be devised to minimize energy by decreasing the operating costs. The consolidation problem is NP-complete, which requires heuristic techniques to get a sub-optimal solution. The authors have proposed a new consolidation scheme for the virtual machines (VMs) by improving the host overload detection phase. The resulting scheme is effective in reducing the energy and the level of Service Level Agreement (SLA) violations both, to a considerable extent. For testing the performance of implementation, a simulation environment is needed that can provide an environment of the actual cloud computing components. The authors have used CloudSim 3.0.3 simulation toolkit that allows testing and analyzing Allocation and Selection algorithms.

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An Efficient Cloud Data Center Allocation to the Source of Requests

Journal of Organizational and End User Computing ◽

10.4018/joeuc.2020070103 ◽

2020 ◽

Vol 32 (3) ◽

pp. 23-36

Author(s):

Kanniga Devi R. ◽

Murugaboopathi Gurusamy ◽

Vijayakumar P.

Keyword(s):

Data Center ◽

Data Centers ◽

Virtual Machines ◽

Service Level Agreement ◽

Cloud Service ◽

Service Level ◽

Cloud Data Center ◽

Cloud Data ◽

Service Consumer ◽

Cloud Data Centers

A Cloud data center is a network of virtualized resources, namely virtualized servers. They provision on-demand services to the source of requests ranging from virtual machines to virtualized storage and virtualized networks. The cloud data center service requests can come from different sources across the world. It is desirable for enhancing Quality of Service (QoS), which is otherwise known as a service level agreement (SLA), an agreement between cloud service requester and cloud service consumer on QoS, to allocate the cloud data center closest to the source of requests. This article models a Cloud data center network as a graph and proposes an algorithm, modified Breadth First Search where the source of requests assigned to the Cloud data centers based on a cost threshold, which limits the distance between them. Limiting the distance between Cloud data centers and the source of requests leads to faster service provisioning. The proposed algorithm is tested for various graph instances and is compared with modified Voronoi and modified graph-based K-Means algorithms that they assign source of requests to the cloud data centers without limiting the distance between them. The proposed algorithm outperforms two other algorithms in terms of average time taken to allocate the cloud data center to the source of requests, average cost and load distribution.

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Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽

10.3390/electronics7120389 ◽

2018 ◽

Vol 7 (12) ◽

pp. 389 ◽

Cited By ~ 6

Author(s):

Aisha Fatima ◽

Nadeem Javaid ◽

Tanzeela Sultana ◽

Waqar Hussain ◽

Muhammad Bilal ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Particle Swarm ◽

Vm Placement ◽

Swarm Optimization ◽

Cloud Data ◽

Cloud Data Centers ◽

Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

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Multidimensional Resource Scheduling in Cloud Data Centers

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.1513 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 1513-1516

Author(s):

Hai Na Song ◽

Xiao Qing Zhang ◽

Zhong Tang He

Keyword(s):

Cloud Computing ◽

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Three Dimensional ◽

Resource Scheduling ◽

Computing Environment ◽

Cloud Computing Environment ◽

Cloud Data ◽

Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

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Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

Mathematical Problems in Engineering ◽

10.1155/2020/7848232 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Xialin Liu ◽

Junsheng Wu ◽

Gang Sha ◽

Shuqin Liu

Keyword(s):

Virtual Machine ◽

Carbon Dioxide Emissions ◽

Data Centers ◽

Virtual Machines ◽

Service Level Agreement ◽

High Capacity ◽

Electrical Energy ◽

Cloud Data ◽

Cloud Data Centers ◽

Vm Consolidation

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.

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Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽

10.3390/s19122724 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2724 ◽

Cited By ~ 3

Author(s):

Yuan ◽

Sun

Keyword(s):

Energy Consumption ◽

Virtual Machine ◽

Ant Colony Algorithm ◽

Data Centers ◽

Virtual Machines ◽

Ant Colony ◽

Virtual Machine Migration ◽

Server Consolidation ◽

Cloud Data ◽

Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

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Service Level Agreement Conformance in Virtual Machine Migration with an Improved Energy and Performance Management Method

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8984 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3904-3906

Author(s):

Susmita J. A. Nair ◽

T. R. Gopalakrishnan Nair

Keyword(s):

Energy Consumption ◽

Performance Management ◽

Large Scale ◽

Data Centers ◽

Virtual Machines ◽

Service Level Agreement ◽

Service Level ◽

Performance Degradation ◽

Virtual Machine Migration ◽

And Performance

Increasing demand of computing resources and the popularity of cloud computing have led the organizations to establish of large-scale data centers. To handle varying workloads, allocating resources to Virtual Machines, placing the VMs in the most suitable physical machine at data centers without violating the Service Level Agreement remains a big challenge for the cloud providers. The energy consumption and performance degradation are the prime focus for the data centers in providing services by strictly following the SLA. In this paper we are suggesting a model for minimizing the energy consumption and performance degradation without violating SLA. The experiments conducted have shown a reduction in SLA violation by nearly 10%.

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SLA and Performance Efficient Heuristics for Virtual Machines Placement in Cloud Data Centers

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2017070102 ◽

2017 ◽

Vol 9 (3) ◽

pp. 17-33 ◽

Cited By ~ 2

Author(s):

Oshin Sharma ◽

Hemraj Saini

Keyword(s):

Energy Consumption ◽

Data Centers ◽

Virtual Machines ◽

Service Level Agreement ◽

Service Level ◽

Consolidation Process ◽

Cloud Data ◽

Trade Offs ◽

And Performance ◽

High Level

Cloud computing has revolutionized the working models of IT industry and increasing the demand of cloud resources which further leads to increase in energy consumption of data centers. Virtual machines (VMs) are consolidated dynamically to reduce the number of host machines inside data centers by satisfying the customer's requirements and quality of services (QoS). Moreover, for using the services of cloud environment every cloud user has a service level agreement (SLA) that deals with energy and performance trade-offs. As, the excess of consolidation and migration may degrade the performance of system, therefore, this paper focuses the overall performance of the system instead of energy consumption during the consolidation process to maintain a trust level between cloud's users and providers. In addition, the paper proposed three different heuristics for virtual machine (VM) placement based on current and previous usage of resources. The proposed heuristics ensure a high level of service level agreements (SLA) and better performance of ESM metric in comparison to previous research.

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