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Journal articles on the topic 'Placement VM'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Li, Bo, and Yun Wang. "An Distributed Virtual Machine Placement Algorithm for Balanced Resource Utilization and Low Energy Consumption." MATEC Web of Conferences 173 (2018): 03092. http://dx.doi.org/10.1051/matecconf/201817303092.

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Virtual machine placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Traditional load balancing or energy-aware VM placement approaches either allocate VMs to PMs in centralized manner or ignore PM’s cost-capacity ratio to implement energy-aware VM placement. We address these two issues by introducing a distributed VM placement approach. A auction-based VM placement algorithm is devised for help VM to find the most suitable server in large heterogeneous cloud data centers. Our algorithm is evaluated by simulation. Experimental

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Filiposka, Sonja, Anastas Mishev, and Carlos Juiz. "Community-based VM placement framework." Journal of Supercomputing 71, no. 12 (2015): 4504–28. http://dx.doi.org/10.1007/s11227-015-1546-1.

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Alashaikh, Abdulaziz, Eisa Alanazi, and Ala Al-Fuqaha. "A Survey on the Use of Preferences for Virtual Machine Placement in Cloud Data Centers." ACM Computing Surveys 54, no. 5 (2021): 1–39. http://dx.doi.org/10.1145/3450517.

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With the rapid development of virtualization techniques, cloud data centers allow for cost-effective, flexible, and customizable deployments of applications on virtualized infrastructure. Virtual machine (VM) placement aims to assign each virtual machine to a server in the cloud environment. VM Placement is of paramount importance to the design of cloud data centers. Typically, VM placement involves complex relations and multiple design factors as well as local policies that govern the assignment decisions. It also involves different constituents including cloud administrators and customers th

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Barthwal, Varun, M. M. S. Rauthan, and Rohan Varma. "A survey on application of machine learning to manage the virtual machines in cloud computing." International Review of Applied Sciences and Engineering 11, no. 3 (2020): 197–208. http://dx.doi.org/10.1556/1848.2020.00065.

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AbstractVirtual machine (VM) management is a fundamental challenge in the cloud datacenter, as it requires not only scheduling and placement, but also optimization of the method to maintain the energy cost and service quality. This paper reviews the different areas of literature that deal with the resource utilization prediction, VM migration, VM placement and the selection of physical machines (PMs) for hosting the VMs. The main features of VM management policies were also examined using a comparative analysis of the current policies. Many research works include Machine Learning (ML) for dete

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Kumar, Kamal, and Jyoti Thaman. "Improving Virtual Machine Migration Effects in Cloud Computing Environments Using Depth First Inspired Opportunity Exploration." International Journal of Cloud Applications and Computing 12, no. 1 (2022): 1–22. http://dx.doi.org/10.4018/ijcac.314209.

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The cloud platform has established itself as the de-facto standard in IT outsourcing. This is resulting in large-scale migration of infrastructure and development platforms from in-house to cloud service providers. Many recent proposals on cloud platforms have addressed several issues that appeared on the cloud horizon. VM placement (VMP) has been a serious concern when it comes to placement of VMs after migration or VM reallocation. Most of the recent works have lacked multiple VM placement (MVMP) problem instances. A recently researched idea of MVMP through depth first opportunistic explorat

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Acharya, Shreenath, and Demian Antony D’Mello. "Energy Saving VM Placement in Cloud." International Journal of Modern Education and Computer Science 10, no. 12 (2018): 28–35. http://dx.doi.org/10.5815/ijmecs.2018.12.04.

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Chen, Yan-Ren, I.-Hsien Liu, Keng-Hao Chang, Chuan-Gang Liu, and Jung-Shian Li. "VM Migration Placement in Cloud Service." Proceedings of International Conference on Artificial Life and Robotics 24 (January 10, 2019): 45–48. http://dx.doi.org/10.5954/icarob.2019.os1-4.

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Baskaran, Nithiya, and Eswari R. "CPU-Memory Aware VM Consolidation for Cloud Data Centers." Scalable Computing: Practice and Experience 21, no. 2 (2020): 159–72. http://dx.doi.org/10.12694/scpe.v21i2.1670.

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The unbalanced usage of resources in cloud data centers cause an enormous amount of power consumption. The Virtual Machine (VM) consolidation shuts the underutilized hosts and makes the overloaded hosts as normally loaded hosts by selecting appropriate VMs from the hosts and migrates them to other hosts in such a way to reduce the energy consumption and to improve physical resource utilization. Efficient method is needed for VM selection and destination hosts selection (VM placement). In this paper, a CPU-Memory aware VM placement algorithm is proposed for selecting suitable destination host f

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Harkesh Sehrawat, Rashmi Sindhu, Vikas Siwach,. "COMPARATIVE ANALYSIS OF VM PLACEMENT AND MIGRATION ALGORITHMS IN VM CONSOLIDATION." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (2021): 479–85. http://dx.doi.org/10.17762/itii.v9i1.156.

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With the increasing number of Internet of Things (IoT) devices, data centers are experiencing immense augmentation in the hardware devices with an increase in the traffic to the cloud infrastructures. To handle this growth and to satisfy users demand, data centers require more energy. The IoT devices produce vast data which needs to be handled properly by the data centers which in turn is responsible for increase in the power consumption at the data centers Management and reduction of this energy is quite a challenging task for the managers and the designers of the data centers as increasing c

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Liu, Zhenpeng, Jiahuan Lu, Nan Su, Bin Zhang, and Xiaofei Li. "Location-Constrained Virtual Machine Placement (LCVP) Algorithm." Scientific Programming 2020 (November 5, 2020): 1–8. http://dx.doi.org/10.1155/2020/8846087.

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Virtual machine (VM) placement is the current day research topic in cloud computing area. In order to solve the problem of imposing location constraints on VMs to meet their requirements in the process of VM placement, the location-constrained VM placement (LCVP) algorithm is proposed in this paper. In LCVP, each VM can only be placed onto one of the specified candidate physical machines (PMs) with enough computing resources and there must be sufficient bandwidth between the selected PMs to meet the communication requirement of the corresponding VMs. Simulation results show that LCVP is feasib

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Fu, Xiong, Qing Zhao, Junchang Wang, Lin Zhang, and Lei Qiao. "Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing." Scientific Programming 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9471356.

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In recent years, high energy consumption has gradually become a prominent problem in a data center. With the advent of cloud computing, computing and storage resources are bringing greater challenges to energy consumption. Virtual machine (VM) initial placement plays an important role in affecting the size of energy consumption. In this paper, we use binary particle swarm optimization (BPSO) algorithm to design a VM placement strategy for low energy consumption measured by proposed energy efficiency fitness, and this strategy needs multiple iterations and updates for VM placement. Finally, the

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Teyeb, Hana, Nejib Ben Hadj-Alouane, Samir Tata, and Ali Balma. "Optimal Dynamic Placement of Virtual Machines in Geographically Distributed Cloud Data Centers." International Journal of Cooperative Information Systems 26, no. 03 (2017): 1750001. http://dx.doi.org/10.1142/s0218843017500010.

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In geo-distributed cloud systems, a key challenge faced by cloud providers is to optimally tune and configure the underlying cloud infrastructure. An important problem in this context, deals with finding an optimal virtual machine (VM) placement, minimizing costs, while at the same time, ensuring good system performance. Moreover, due to the fluctuations of demand and traffic patterns, it is crucial to dynamically adjust the VM placement scheme over time. It should be noted that most of the existing studies, however, dealt with this problem either by ignoring its dynamic aspect or by proposing

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Fatima, Aisha, Nadeem Javaid, Tanzeela Sultana, et al. "Virtual Machine Placement via Bin Packing in Cloud Data Centers." Electronics 7, no. 12 (2018): 389. http://dx.doi.org/10.3390/electronics7120389.

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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

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Sharma, Oshin, and Hemraj Saini. "Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment." International Journal of Business Data Communications and Networking 13, no. 1 (2017): 45–57. http://dx.doi.org/10.4018/ijbdcn.2017010104.

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In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, th

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Jing, Si Yuan, Shahzad Ali, and Kun She. "Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center." Applied Mechanics and Materials 325-326 (June 2013): 1730–33. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1730.

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Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption b

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Kumar, Kamal, and Jyoti Thaman. "Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment." International Journal of Grid and High Performance Computing 12, no. 4 (2020): 13–34. http://dx.doi.org/10.4018/ijghpc.2020100102.

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Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution f

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Garg, Neha, Neeraj, Manish Raj, Indrajeet Gupta, Vinay Kumar, and G. R. Sinha. "Energy-Efficient Scientific Workflow Scheduling Algorithm in Cloud Environment." Wireless Communications and Mobile Computing 2022 (March 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/1637614.

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Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requireme

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Ge, Jun Wei, Hai Ming Zheng, and Yi Qiu Fang. "A Hybird Virtual Machine Placement Aglrithm for Virtualized Desktop Infrastructure." Advanced Materials Research 760-762 (September 2013): 1906–10. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1906.

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As we all kown, The virtual machine placement is one kind of bin-packing problem. By optimizing placement of virtual machine. We can improve VM performance, enhance resource utilization, reduce energy comsumption. After analysis the existing virtual machine placement aglrithm. We propose a hybird virtual machine placement aglrithm (HTA) which based on network latency threshold for the requirement of low network latence and low VM migraiton ratio in Virtualized Desktop Infrastructure. It elect qualified node set based on network latency threshold and palce the virtual machines with load-balance

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Mohamed, Marwa F., Mai Dahshan, Kenli Li, and Ahmad Salah. "Virtual Machine Replica Placement Using a Multiobjective Genetic Algorithm." International Journal of Intelligent Systems 2023 (June 28, 2023): 1–16. http://dx.doi.org/10.1155/2023/8378850.

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Virtual machine (VM) replication is a critical task in any cloud computing platform to ensure the availability of the cloud service for the end user. In this task, one primary VM resides on a physical machine (PM) and one or more replicas reside on separate PMs. In cloud computing, VM placement (VMP) is a well-studied problem in terms of different goals, such as power consumption reduction. The VMP problem can be solved by using heuristics, namely, first-fit and meta-heuristics such as the genetic algorithm. Despite extensive research into the VMP problem, there are few works that consider VM

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Rahman, Mahfuzur, and Peter Graham. "Compatibility-based static VM placement minimizing interference." Journal of Network and Computer Applications 84 (April 2017): 68–81. http://dx.doi.org/10.1016/j.jnca.2017.02.004.

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Chen, Lixia, Jian Li, Ruhui Ma, Haibing Guan, and Hans-Arno Jacobsen. "Balancing Power And Performance In HPC Clouds." Computer Journal 63, no. 6 (2020): 880–99. http://dx.doi.org/10.1093/comjnl/bxz150.

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Abstract With energy consumption in high-performance computing clouds growing rapidly, energy saving has become an important topic. Virtualization provides opportunities to save energy by enabling one physical machine (PM) to host multiple virtual machines (VMs). Dynamic voltage and frequency scaling (DVFS) is another technology to reduce energy consumption. However, in heterogeneous cloud environments where DVFS may be applied at the chip level or the core level, it is a great challenge to combine these two technologies efficiently. On per-core DVFS servers, cloud managers should carefully de

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J. A. Nair, Susmita, and T. R. Gopalakrishnan Nair. "Performance degradation assessment and VM placement policy in cloud." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4961. http://dx.doi.org/10.11591/ijece.v9i6.pp4961-4969.

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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 eval

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Cai, Yu. "A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing." International Journal of Green Computing 8, no. 2 (2017): 20–36. http://dx.doi.org/10.4018/ijgc.2017070102.

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Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the num

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Sheetal, Annabathula Phani, and Kongara Ravindranath. "High Efficient Virtual Machine Migration Using Glow Worm Swarm Optimization Method for Cloud Computing." Ingénierie des systèmes d information 26, no. 6 (2021): 591–97. http://dx.doi.org/10.18280/isi.260610.

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In this paper, high efficient Virtual Machine (VM) migration using GSO algorithm for cloud computing is proposed. This algorithm contains 3 phases: (i) VM selection, (ii) optimum number of VMs selection, (iii) VM placement. In VM selection phase, VMs to be migrated are selected based on their resource utilization and fault probability. In phase-2, optimum number of VMs to be migrated are determined based on the total power consumption. In VM placement phase, Glowworm Swarm Optimization (GSO) is used for finding the target VMs to place the migrated VMs. The fitness function is derived in terms

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Pushpa R. and M. Siddappa. "An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm." International Journal of Ambient Computing and Intelligence 12, no. 3 (2021): 16–38. http://dx.doi.org/10.4018/ijaci.2021070102.

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In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by movi

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Kaaouache, Mohamed Amine, and Sadok Bouamama. "An energy-efficient VM placement method for cloud data centers using a hybrid genetic algorithm." Journal of Systems and Information Technology 20, no. 4 (2018): 430–45. http://dx.doi.org/10.1108/jsit-10-2017-0089.

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Purpose This purpose of this paper is to propose a novel hybrid genetic algorithm based on a virtual machine (VM) placement method to improve energy efficiency in cloud data centers. How to place VMs on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. Over the past few years, many approaches for VM placement (VMP) have been proposed; however, existing VM placement approaches only consider energy consumption by PMs, and do not consider the energy consumption of the communication network of a data center. Desi

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Alsbatin, Loiy, Gürcü Öz, and Ali Ulusoy. "Efficient virtual machine placement algorithms for consolidation in cloud data centers." Computer Science and Information Systems 17, no. 1 (2020): 29–50. http://dx.doi.org/10.2298/csis181122036a.

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Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload t

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J A Nair, Susmita, and T. R. Gopalakrishnan Nair. "VM placement with effective energy management in cloud using optimal VM allocation framework (OVAF)." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1531. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1531-1538.

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<p class="CAbstract"><span>The performance of servers at the data centers is affected when the servers are overloaded. To overcome this problem, the workload at the overloaded servers has to be redistributed to other servers which is possible with live VM migration. Live migration plays a crucial role in handling the overload at the data centers without service interruption. However, live migration also incurs some performance loss and energy overhead. The energy consumption at the data centers is a matter of utmost concern both in terms of economy and ecology. In this paper we are

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Sridharan R. and Domnic S. "Placement for Intercommunicating Virtual Machines in Autoscaling Cloud Infrastructure." Journal of Organizational and End User Computing 33, no. 2 (2021): 17–35. http://dx.doi.org/10.4018/joeuc.20210301.oa2.

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Due to pay-as-you-go style adopted by cloud datacenters (DC), modern day applications having intercommunicating tasks depend on DC for their computing power. Due to unpredictability of rate at which data arrives for immediate processing, application performance depends on autoscaling service of DC. Normal VM placement schemes place these tasks arbitrarily onto different physical machines (PM) leading to unwanted network traffic resulting in poor application performance and increases the DC operating cost. This paper formulates autoscaling and intercommunication aware task placements (AIATP) as

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Thakor, Devendra, and Dipak Dabhi. "Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres." International Journal of Grid and Utility Computing 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijguc.2022.10049114.

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Dabhi, Dipak, and Devendra Thakor. "Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres." International Journal of Grid and Utility Computing 13, no. 5 (2022): 459. http://dx.doi.org/10.1504/ijguc.2022.126189.

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Kaur, Ramandeep, and Navpreet Kaur. "A Study for VM Placement Schemes in Cloud." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 208. http://dx.doi.org/10.23956/ijarcsse.v7i8.52.

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The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reactio

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Mishra, Sambit Kumar, Deepak Puthal, Bibhudatta Sahoo, et al. "Energy-efficient VM-placement in cloud data center." Sustainable Computing: Informatics and Systems 20 (December 2018): 48–55. http://dx.doi.org/10.1016/j.suscom.2018.01.002.

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Kim, Seontae, and Young-ri Choi. "Constraint-aware VM placement in heterogeneous computing clusters." Cluster Computing 23, no. 1 (2019): 71–85. http://dx.doi.org/10.1007/s10586-019-02966-6.

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Chen, Lei, Jing Zhang, Lijun Cai, Rui Li, Tingqin He, and Tao Meng. "MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement." International Journal of Distributed Sensor Networks 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/679170.

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Cloud data centers are facing increasingly virtual machine (VM) placement problems, such as high energy consumption, imbalanced utilization of multidimension resource, and high resource wastage rate. In order to solve the virtual machine placement problems in large scale, three algorithms are proposed. Firstly, we propose a physical machine (PM) classification algorithm by analyzing pseudotime complexity and find out an important factor (the number of physical hosts) that affects the efficiency, which improves running efficiency through reduction number of physical hosts; secondly, we present

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Baskaran, Nithiya, and Eswari R. "Efficient VM Selection Strategies in Cloud Datacenter Using Fuzzy Soft Set." Journal of Organizational and End User Computing 33, no. 5 (2021): 153–79. http://dx.doi.org/10.4018/joeuc.20210901.oa8.

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A cloud data center is established to meet the storage demand due to the rate of growth of data. The inefficient use of resources causes an enormous amount of power consumption in data centers. In this paper, a fuzzy soft set-based virtual machine (FSS_VM) consolidation algorithm is proposed to address this problem. The algorithm uses four thresholds to detect overloaded hosts and applies fuzzy soft set approach to select appropriate VM for migration. It considers all factors: CPU utilization, memory usage, RAM usage, and correlation values. The algorithm is experimentally tested for 11 differ

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B.Rathod, Suresh, and V. Krishna Reddy. "Decision Making Framework for Decentralized Virtual Machine Placement in Cloud Computing." International Journal of Engineering & Technology 7, no. 2.7 (2018): 705. http://dx.doi.org/10.14419/ijet.v7i2.7.10926.

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In distributed cloud environment hosts are configured with Local Resource Monitors (LRM). This LRM monitors the underlying hosts’ resource usage, runs independently and balances the underling host’s load by migrating Virtual Machine (VM) instance. For the dynamic environment, each hosts has varying resource requirement, hosts load cannot remain constant. LRM at each host takes decision for VM migration considering static threshold on its own and other hosts current CPU utilization. This result in chances of getting selected same host for VM placement by multiple hosts to reduce resource usage

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Rahimi, Amin, Leili Mohammad Khanli, and Saeid Pashazadeh. "Energy efficient virtual machine placement algorithm with balanced resource utilization based on priority of resources." Computer Engineering and Applications Journal 4, no. 2 (2015): 107–18. http://dx.doi.org/10.18495/comengapp.v4i2.134.

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The increasing energy consumption has become a major concern in cloud computing due to its cost and environmental damage. Virtual Machine placement algorithms have been proven to be very effective in increasing energy efficiency and thus reducing the costs. In this paper we have introduced a new priority routing VM placement algorithm and have compared it with PABFD (power-aware best fit decreasing) on CoMon dataset using CloudSim for simulation. Our experiments show the superiority of our new method with regards to energy consumption and level of SLA violations measures and prove that priorit

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Deng, Li, Yang Li, Li Yao, Yu Jin, and Jinguang Gu. "Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing." Mobile Information Systems 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4859862.

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Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placem

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Chen, Tao, Xiaofeng Gao, and Guihai Chen. "Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks." Scientific Programming 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3101658.

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Virtualization has been an efficient method to fully utilize computing resources such as servers. The way of placing virtual machines (VMs) among a large pool of servers greatly affects the performance of data center networks (DCNs). As network resources have become a main bottleneck of the performance of DCNs, we concentrate on VM placement with Traffic-Aware Balancing to evenly utilize the links in DCNs. In this paper, we first proposed a Virtual Machine Placement Problem with Traffic-Aware Balancing (VMPPTB) and then proved it to be NP-hard and designed a Longest Processing Time Based Place

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Sansanwal, Suman, and Nitin Jain. "A comprehensive survey on load balancing techniques for virtual machines." System research and information technologies, no. 4 (December 26, 2023): 135–47. http://dx.doi.org/10.20535/srit.2308-8893.2023.4.10.

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Cloud computing is an emerging technique with remarkable features such as scalability, high flexibility, and reliability. Since this field is growing exponentially, more users are attracted to fast and better service. Virtual Machine (VM) allocation plays a crucial role in cloud computing optimization; hence, resource distribution is not impacted by machine failure and is migrated with no downtime. Therefore, effective management of virtual machines is necessary for increasing profit, energy-saving, etc. However, it could utilize the virtual machine resources more efficiently because of the in

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Theja, Perla Ravi, and S. K. Khadar Babu. "Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers." Cybernetics and Information Technologies 16, no. 2 (2016): 97–112. http://dx.doi.org/10.1515/cait-2016-0023.

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Abstract The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service (QoS) oriented computing platform. To meet these requirements, virtualization and Virtual Machine (VM) consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing

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Ashwin Kumar Sarma, V., Rahul Rajendra, P. Dheepan, and K. S. Sendhil Kumar. "An Optimal Ant Colony Algorithm for Efficient VM Placement." Indian Journal of Science and Technology 8, S2 (2015): 156. http://dx.doi.org/10.17485/ijst/2015/v8is2/60286.

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Chen, Xiang, Jun-rong Tang, and Yong Zhang. "Towards A Virtual Machine Migration Algorithm Based On Multi-Objective Optimization." International Journal of Mobile Computing and Multimedia Communications 8, no. 3 (2017): 79–89. http://dx.doi.org/10.4018/ijmcmc.2017070106.

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In the cloud computing, the virtual machine (VM) dynamical management method needs to consider VM resource re-configuration caused by system computation resource status changing and load fluctuation. Based on migration objectives as QoS (Quality of Service), resource competition and energy consumption, the VM migration time, migration objective node selection and VM placement strategies are designed in this work. The Multi-Criteria Decision-Making (MCDM) method is also introduced for migration destination host selection. Experiment results show that the multi-objective optimization management

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Chen, Wei, Xiaoqiang Qiao, Jun Wei, Hua Zhong, and Tao Huang. "A Virtual Machine Placement and Reconfiguration Framework for Cloud Computing Platforms." International Journal of Adaptive, Resilient and Autonomic Systems 5, no. 2 (2014): 1–22. http://dx.doi.org/10.4018/ijaras.2014040101.

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As a rising application paradigm and technology, cloud computing can leverage the efficient pooling of on-demand, self-managed virtual infrastructure. How to maximize the resource utilization and how to reduce the cost of configuration are essential issues in cloud computing. In this paper, the authors propose a framework to achieve these objectives by optimizing VM placement and deciding when and how to perform the VM reconfigurations. The authors leverage the vector arithmetic to model the objective of balancing the multiple resource utilization and propose an optimization method for the sta

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Satveer and Mahendra Singh Aswal. "VM Consolidation Plan for Improving the Energy Efficiency of Cloud." Cybernetics and Information Technologies 21, no. 3 (2021): 145–59. http://dx.doi.org/10.2478/cait-2021-0035.

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Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumpti

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Li, Huixi, Yinhao Xiao, and YongLuo Shen. "Learning-Based Virtual Machine Selection in Cloud Server Consolidation." Mathematical Problems in Engineering 2022 (September 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/6853196.

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In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection, and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects ap

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Narantuya, Jargalsaikhan, Taejin Ha, Jaewon Bae, and Hyuk Lim. "Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center." Applied Sciences 9, no. 16 (2019): 3223. http://dx.doi.org/10.3390/app9163223.

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In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency

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Li, Zhihua, Meini Pan, and Lei Yu. "Multi-resource collaborative optimization for adaptive virtual machine placement." PeerJ Computer Science 8 (January 6, 2022): e852. http://dx.doi.org/10.7717/peerj-cs.852.

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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

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Kumar, Narander, and Swati Saxena. "Energy-Efficient Load-Aware VM Placement using Multi-Metrics Analysis." Indian Journal of Science and Technology 10, no. 32 (2017): 1–8. http://dx.doi.org/10.17485/ijst/2017/v10i32/113054.

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