Relevant bibliographies by topics / Placement VM

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Placement VM'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Placement VM"

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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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Dissertations / Theses on the topic "Placement VM"

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Thulo, Motlatsi Isaac. "Optimized Security-aware VM placement algorithm." Diss., University of Pretoria, 2019. http://hdl.handle.net/2263/73387.

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The rapidly increasing dependency on use of clouds results in Cloud Service Providers (CSPs) having to deal with high cloud services demands. To meet these demands, CSPs take advantage of virtualization technology to provide a seemingly unlimited pool of computing resources. This technology consolidates multiple instances of Virtual Machines (VMs) into the same Physical Machines (PMs) and share physical computing resources. To guarantee customer satisfaction, CSPs need to ensure optimized cloud environment that provides good Quality of Services (QoS) which conform to the performance levels sti
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Atchukatla, Mahammad suhail. "Algorithms for efficient VM placement in data centers : Cloud Based Design and Performance Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17221.

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Content: Recent trends show that cloud computing adoption is continuously increasing in every organization. So, demand for the cloud datacenters tremendously increases over a period, resulting in significantly increased resource utilization of the datacenters. In this thesis work, research was carried out on optimizing the energy consumption by using packing of the virtual machines in the datacenter. The CloudSim simulator was used for evaluating bin-packing algorithms and for practical implementation OpenStack cloud computing environment was chosen as the platform for this research.   Objecti
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Mantri, Sushil Govindnarayan. "Efficient In-Depth IO Tracing and its application for optimizing systems." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50145.

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Understanding user and system behavior is most vital for designing efficient systems. Most systems are designed with certain user workload in mind. However, such workloads evolve over time, or the underlying hardware assumptions change. Further, most modern systems are not built or deployed in isolation, they interact with other systems whose behavior might not be exactly understood. Thus in order to understand the performance of a system, it must be inspected closely while user workloads are running. Such close inspection must be done with minimum disturbance to the user workload. Thus tracin
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Al-ou'n, Ashraf M. S. "VM Allocation in Cloud Datacenters Based on the Multi-Agent System. An Investigation into the Design and Response Time Analysis of a Multi-Agent-based Virtual Machine (VM) Allocation/Placement Policy in Cloud Datacenters." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16067.

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Recent years have witnessed a surge in demand for infrastructure and services to cover high demands on processing big chunks of data and applications resulting in a mega Cloud Datacenter. A datacenter is of high complexity with increasing difficulties to identify, allocate efficiently and fast an appropriate host for the requested virtual machine (VM). Establishing a good awareness of all datacenter’s resources enables the allocation “placement” policies to make the best decision in reducing the time that is needed to allocate and create the VM(s) at the appropriate host(s). However, current a
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Ahvar, Shohreh. "Resource allocation in cloud and Content Delivery Network (CDN)." Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0011.

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L’objectif de cette thèse est de présenter de nouveaux algorithmes de répartition des ressources sous la forme de machines virtuelles (VMs) et fonction de réseau virtuel (VNFs) dans les Clouds et réseaux de diffusion de contenu (CDNs). La thèse comprend deux principales parties: la première se concentre sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. Cette partie comprend quatre contributions. La première contribution est une étude de l’état de l’art sur la répartition des coûts et des émissions de carbone dan
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Ouarnoughi, Hamza. "Placement autonomique de machines virtuelles sur un système de stockage hybride dans un cloud IaaS." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0055/document.

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Les opérateurs de cloud IaaS (Infrastructure as a Service) proposent à leurs clients des ressources virtualisées (CPU, stockage et réseau) sous forme de machines virtuelles (VM). L’explosion du marché du cloud les a contraints à optimiser très finement l’utilisation de leurs centres de données afin de proposer des services attractifs à moindre coût. En plus des investissements liés à l’achat des infrastructures et de leur coût d’utilisation, la consommation énergétique apparaît comme un point de dépense important (2% de la consommation mondiale) et en constante augmentation. Sa maîtrise représ
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Mengistu, Tessema Mindaye. "RESOURCE MANAGEMENT FRAMEWORK FOR VOLUNTEER CLOUD COMPUTING." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1613.

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The need for high computing resources is on the rise, despite the exponential increase of the computing capacity of workstations, the proliferation of mobile devices, and the omnipresence of data centers with massive server farms that housed tens (if not hundreds) of thousands of powerful servers. This is mainly due to the unprecedented increase in the number of Internet users worldwide and the Internet of Things (IoTs). So far, Cloud Computing has been providing the necessary computing infrastructures for applications, including IoT applications. However, the current cloud infrastructures tha
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Ahvar, Shohreh. "Resource allocation in cloud and Content Delivery Network (CDN)." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0011.

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L’objectif de cette thèse est de présenter de nouveaux algorithmes de répartition des ressources sous la forme de machines virtuelles (VMs) et fonction de réseau virtuel (VNFs) dans les Clouds et réseaux de diffusion de contenu (CDNs). La thèse comprend deux principales parties: la première se concentre sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. Cette partie comprend quatre contributions. La première contribution est une étude de l’état de l’art sur la répartition des coûts et des émissions de carbone dan
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Ahvar, Ehsan. "Cost-efficient resource allocation for green distributed clouds." Thesis, Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0001.

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L'objectif de cette thèse est de présenter de nouveaux algorithmes de placement de machines virtuelles (VMs) à fin d’optimiser le coût et les émissions de carbone dans les Clouds distribués. La thèse se concentre d’abord sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. La thèse comprend deux principales parties: la première propose, développe et évalue les algorithmes de placement statiques de VMs (où un premier placement d'une VM détient pendant toute la durée de vie de la VM). La deuxième partie propose des a
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Ahvar, Ehsan. "Cost-efficient resource allocation for green distributed clouds." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0001.

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L'objectif de cette thèse est de présenter de nouveaux algorithmes de placement de machines virtuelles (VMs) à fin d’optimiser le coût et les émissions de carbone dans les Clouds distribués. La thèse se concentre d’abord sur la rentabilité des Clouds distribués, et développe ensuite les raisons d’optimiser les coûts ainsi que les émissions de carbone. La thèse comprend deux principales parties: la première propose, développe et évalue les algorithmes de placement statiques de VMs (où un premier placement d'une VM détient pendant toute la durée de vie de la VM). La deuxième partie propose des a
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Book chapters on the topic "Placement VM"

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Chauhan, Neeru, Nitin Rakesh, and Rakesh Matam. "Assessment on VM Placement and VM Selection Strategies." In Nature Inspired Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6747-1_18.

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Filiposka, Sonja, Anastas Mishev, and Carlos Juiz. "Balancing Performances in Online VM Placement." In ICT Innovations 2015. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25733-4_16.

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Tsakalozos, Konstantinos, Mema Roussopoulos, and Alex Delis. "VM Placement in non-Homogeneous IaaS-Clouds." In Service-Oriented Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25535-9_12.

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Alkhalaf, Asma, and Farookh Khadeer Hussain. "Towards Priority VM Placement in Fog Networks." In Advanced Information Networking and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57870-0_36.

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Wu, Jiahuai, and Hong Shen. "Efficient Algorithms for VM Placement in Cloud Data Center." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6442-5_32.

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Dutta, Debojyoti, Michael Kapralov, Ian Post, and Rajendra Shinde. "Embedding Paths into Trees: VM Placement to Minimize Congestion." In Algorithms – ESA 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33090-2_38.

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Youn, Chan-Hyun, Min Chen, and Patrizio Dazzi. "VM Placement via Resource Brokers in a Cloud Datacenter." In KAIST Research Series. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5071-8_2.

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Lin, Xiuyan, Zhanghui Liu, and Wenzhong Guo. "Energy-Efficient VM Placement Algorithms for Cloud Data Center." In Cloud Computing and Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28430-9_4.

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Tandon, Akanksha, Sudhanshu Kulshresha, and Sanjeev Patel. "A Review on VM Placement Scheme Using Optimization Algorithms." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2_3.

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Dabhi, Dipak, and Devendra Thakor. "Energy and SLA-Aware VM Placement Policy for VM Consolidation Process in Cloud Data Centers." In Sustainable Technology and Advanced Computing in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4364-5_26.

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Conference papers on the topic "Placement VM"

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Dabbagh, Mehiar, Bechir Hamdaoui, Mohsen Guizani, and Ammar Rayes. "Release-time aware VM placement." In 2014 IEEE Globecom Workshops (GC Wkshps). IEEE, 2014. http://dx.doi.org/10.1109/glocomw.2014.7063417.

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Chowdhury, Mohammed Rashid, Mohammad Raihan Mahmud, and Rashedur M. Rahman. "Clustered based VM placement strategies." In 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS). IEEE, 2015. http://dx.doi.org/10.1109/icis.2015.7166601.

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Al-Ou'n, Ashraf, Mariam Kiran, and Demetres D. Kouvatsos. "Using Agent-Based VM Placement Policy." In 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2015. http://dx.doi.org/10.1109/ficloud.2015.110.

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Calcavecchia, Nicolo Maria, Ofer Biran, Erez Hadad, and Yosef Moatti. "VM Placement Strategies for Cloud Scenarios." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.113.

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Cucinotta, Tommaso, Luigi Pannocchi, Filippo Galli, Silvia Fichera, Sourav Lahiri, and Antonino Artale. "Optimum VM Placement for NFV Infrastructures." In 2022 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2022. http://dx.doi.org/10.1109/ic2e55432.2022.00029.

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Hossain, Sajjad, Md Mahfuzur Rahman, and Md Musfique Anwar. "Interference-Aware VM Placement in Cloud." In 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS). IEEE, 2023. http://dx.doi.org/10.1109/icdcs57875.2023.00141.

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Gupta, A., L. V. Kale, D. Milojicic, P. Faraboschi, and S. M. Balle. "HPC-Aware VM Placement in Infrastructure Clouds." In 2013 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2013. http://dx.doi.org/10.1109/ic2e.2013.38.

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Wang, Yi, and Ye Xia. "Energy Optimal VM Placement in the Cloud." In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0021.

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Kaur, Amandeep, and Mala Kalra. "Energy optimized VM placement in cloud environment." In 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence). IEEE, 2016. http://dx.doi.org/10.1109/confluence.2016.7508103.

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Kabir, Md Humayun, Gholamali C. Shoja, and Sudhakar Ganti. "VM Placement Algorithms for Hierarchical Cloud Infrastructure." In 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2014. http://dx.doi.org/10.1109/cloudcom.2014.53.

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