Academic literature on the topic 'Virtual Machines (VM)'
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Journal articles on the topic "Virtual Machines (VM)"
Hasan, Waqas. "Virtual Machine Migration in Cloud Computing." Oriental journal of computer science and technology 14, no. 010203 (February 28, 2022): 46–51. http://dx.doi.org/10.13005/ojcst14.010203.06.
Full textHasan, Waqas. "A Survey on Virtual Machine Migration in Cloud Computing." International Journal of Scientific & Engineering Research 13, no. 03 (March 25, 2022): 648–53. http://dx.doi.org/10.14299/ijser.2022.03.03.
Full textGe, 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.
Full textChen, Ji-Ming, Shi Chen, Xiang Wang, Lin Lin, and Li Wang. "A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks." Security and Communication Networks 2021 (December 21, 2021): 1–17. http://dx.doi.org/10.1155/2021/2729949.
Full textLiu, Yanbing, Bo Gong, Congcong Xing, and Yi Jian. "A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/973069.
Full textLiu, 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.
Full textT. Y. J., Naga Malleswari, Senthil Kumar T., and JothiKumar C. "Resumption of virtual machines after adaptive deduplication of virtual machine images in live migration." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 654. http://dx.doi.org/10.11591/ijece.v11i1.pp654-663.
Full textSushmitha, G. M. Karthik, and M. Sayeekumar. "Power and Performance Based Genetic Ant Colony Algorithm for Virtual Machine Placement." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 32–36. http://dx.doi.org/10.1166/jctn.2020.8625.
Full textSrinivasa Rao, L., and I. Raviprakash Reddy. "A novel energy efficient virtual machine configuration and migration technique." International Journal of Engineering & Technology 7, no. 4 (September 17, 2018): 2391. http://dx.doi.org/10.14419/ijet.v7i4.13236.
Full textMuhammad, Shoaib, Muhammad Nabeel Mustafa Syed, and Shabhi Ul Hasan Naqvi Syed. "Techniques of migration in live virtual machine and its challenges." i-manager's Journal on Computer Science 9, no. 4 (2022): 31. http://dx.doi.org/10.26634/jcom.9.4.18540.
Full textDissertations / Theses on the topic "Virtual Machines (VM)"
George, Sharath. "Usermode kernel : running the kernel in userspace in VM environments." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2858.
Full textYoginath, Srikanth B. "Virtual time-aware virtual machine systems." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52321.
Full textAtchukatla, 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.
Full text- Perform simulation of algorithms in CloudSim simulator. Estimate and compare the energy consumption of different packing algorithms. Design an OpenStack testbed to implement the Bin packing algorithm. Methods: We use CloudSim simulator to estimate the energy consumption of the First fit, the First fit decreasing, Best fit and Enhanced best-fit algorithms. Design a heuristic model for implementation in the OpenStack environment for optimizing the energy consumption for the physical machines. Server consolidation and live migration are used for the algorithms design in the OpenStack implementation. Our research also extended to the Nova scheduler functionality in an OpenStack environment. Results: Most of the case the enhanced best-fit algorithm gives the better results. The results are obtained from the default OpenStack VM placement algorithm as well as from the heuristic algorithm developed in this simulation work. The comparison of results indicates that the total energy consumption of the data center is reduced without affecting potential service level agreements. Conclusions: The research tells that energy consumption of the physical machines can be optimized without compromising the offered service quality. A Python wrapper was developed to implement this model in the OpenStack environment and minimize the energy consumption of the Physical machine by shutdown the unused physical machines. The results indicate that CPU Utilization does not vary much when live migration of the virtual machine is performed.
Ducasse, Quentin. "Sécurisation matérielle de la compilation à la volée des machines virtuelles langage." Electronic Thesis or Diss., Brest, École nationale supérieure de techniques avancées Bretagne, 2024. https://theses.hal.science/tel-04690882.
Full textLanguage Virtual Machines (VMs) are the run-time environment of popular high level managed languages. They offer portability and memory handling for the developer and are deployed on most computing devices. Their widespread distribution, handling of untrusted user inputs, and low-level task execution make them interesting to attackers. Software-only solutions that isolate their different components often incur a high performance overhead incompatible with just-in-time (JIT) compilation. Hardware-accelerated run time protections are pushed in vendor processors as a solution to conciliate strong security guarantees with performance. To allow experimentation in the design and comparison of such solutions, this thesis is interested in the RISC-V instruction set and its extension capabilities. We present Gigue, a workload generator that outputs binaries similar to JIT code directly executable on RISC-V softcores. It provides an interface for custom instructions and guarantees their execution. We present an instruction-level domain isolation solution added to Gigue binaries and implemented in an application-class processor with processor modifications. The solution adds negligible performance overhead while enforcing strong properties on domains. As an effort to motivate deployment in real use cases, we extend the Pharo JIT compiler to the RISC-V instruction set along with its testing infrastructure
Ahvar, Ehsan. "Cost-efficient resource allocation for green distributed clouds." Thesis, Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0001.
Full textVirtual machine (VM) placement (i.e., resource allocation) method has a direct effect on both cost and carbon emission. Considering the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of VMs for cost and carbon efficiency even more critical and complex than in centralized clouds. The goal of this thesis is to present new VM placement algorithms to optimize cost and carbon emission in a distributed cloud. It first focuses on cost efficiency in distributed clouds and, then, extends the goal to optimization of both cost and carbon emission at the same time. Thesis includes two main parts. The first part of thesis proposes, develops and evaluates static VM placement algorithms to reach the mentioned goal where an initial placement of a VM holds throughout the lifetime of the VM. The second part proposes dynamic VM placement algorithms where the initial placement of VMs is allowed to change (e.g., through VM migration and consolidation). The first contribution is a survey of the state of the art on cost and carbon emission resource allocation in distributed cloud environments. The second contribution targets the challenge of optimizing inter-DC communication cost for large-scale tasks and proposes a Network-Aware Cost-Efficient Resource allocation method, called NACER, for distributed clouds. The goal is to minimize the network communication cost of running a task in a distributed cloud by selecting the DCs to provision the VMs in such a way that the total network distance (hop count or any reasonable measure) among the selected DCs is minimized. The third contribution proposes a Network-Aware Cost Efficient VM Placement method (called NACEV) for Distributed Clouds. NACEV is an extended version of NACER. While NACER only considers inter-DC communication cost, NACEV optimizes both communication and computing cost at the same time and also proposes a mapping algorithm to place VMs on Physical Machines (PMs) inside of the selected DCs. NACEV also considers some aspects such as heterogeneity of VMs, PMs and switches, variety of energy prices, multiple paths between PMs, effects of workload on cost (energy consumption) of cloud devices (i.e., switches and PMs) and also heterogeneity of energy model of cloud elements. The forth contribution presents a Cost and Carbon Emission-Efficient VM Placement Method (called CACEV) for green distributed clouds. CACEV is an extended version of NACEV. In addition to cost efficiency, CACEV considers carbon emission efficiency and green distributed clouds. It is a VM placement algorithm for joint optimization of computing and network resources, which also considers price, location and carbon emission rate of resources. It also, unlike previous contributions of thesis, considers IaaS Service Level Agreement (SLA) violation in the system model. To get a better performance, the fifth contribution proposes a dynamic Cost and Carbon Emission-Efficient VM Placement method (D-CACEV) for green distributed clouds. D-CACEV is an extended version of our previous work, CACEV, with additional figures, description and also live VM migration mechanisms. We show that our joint VM placement-reallocation mechanism can constantly optimize both cost and carbon emission at the same time in a distributed cloud
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.
Full textVirtual machine (VM) placement (i.e., resource allocation) method has a direct effect on both cost and carbon emission. Considering the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of VMs for cost and carbon efficiency even more critical and complex than in centralized clouds. The goal of this thesis is to present new VM placement algorithms to optimize cost and carbon emission in a distributed cloud. It first focuses on cost efficiency in distributed clouds and, then, extends the goal to optimization of both cost and carbon emission at the same time. Thesis includes two main parts. The first part of thesis proposes, develops and evaluates static VM placement algorithms to reach the mentioned goal where an initial placement of a VM holds throughout the lifetime of the VM. The second part proposes dynamic VM placement algorithms where the initial placement of VMs is allowed to change (e.g., through VM migration and consolidation). The first contribution is a survey of the state of the art on cost and carbon emission resource allocation in distributed cloud environments. The second contribution targets the challenge of optimizing inter-DC communication cost for large-scale tasks and proposes a Network-Aware Cost-Efficient Resource allocation method, called NACER, for distributed clouds. The goal is to minimize the network communication cost of running a task in a distributed cloud by selecting the DCs to provision the VMs in such a way that the total network distance (hop count or any reasonable measure) among the selected DCs is minimized. The third contribution proposes a Network-Aware Cost Efficient VM Placement method (called NACEV) for Distributed Clouds. NACEV is an extended version of NACER. While NACER only considers inter-DC communication cost, NACEV optimizes both communication and computing cost at the same time and also proposes a mapping algorithm to place VMs on Physical Machines (PMs) inside of the selected DCs. NACEV also considers some aspects such as heterogeneity of VMs, PMs and switches, variety of energy prices, multiple paths between PMs, effects of workload on cost (energy consumption) of cloud devices (i.e., switches and PMs) and also heterogeneity of energy model of cloud elements. The forth contribution presents a Cost and Carbon Emission-Efficient VM Placement Method (called CACEV) for green distributed clouds. CACEV is an extended version of NACEV. In addition to cost efficiency, CACEV considers carbon emission efficiency and green distributed clouds. It is a VM placement algorithm for joint optimization of computing and network resources, which also considers price, location and carbon emission rate of resources. It also, unlike previous contributions of thesis, considers IaaS Service Level Agreement (SLA) violation in the system model. To get a better performance, the fifth contribution proposes a dynamic Cost and Carbon Emission-Efficient VM Placement method (D-CACEV) for green distributed clouds. D-CACEV is an extended version of our previous work, CACEV, with additional figures, description and also live VM migration mechanisms. We show that our joint VM placement-reallocation mechanism can constantly optimize both cost and carbon emission at the same time in a distributed cloud
Hu, Ji. "A virtual machine architecture for IT-security laboratories." Phd thesis, [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980935652.
Full textAlbaaj, Hassan, and Victor Berggren. "Benchmark av Containers och Unikernels." Thesis, Tekniska Högskolan, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-50214.
Full textSyfte – Syftet med denna studie är att undersöka möjligheten att effektivisera lokala nätverk och databaser med hjälp av unikernels och att jämföra denna möjlighet med containrar. Detta kan även gälla utveckling av programvara för att säkerställa att programvaran exekveras på servern på exakt samma sätt som den tidigare gjort lokalt på utvecklarens lokala dator. Metod – Två experiment utförs för att undersöka om det går besvara syftet, kvantitativa data samlas in i båda fallen, datan är även redovisad kvantitativt. Python-script används föratt starta C-script som agerar klient och server. Tidtagning på algoritmer i unikernels respektive containrar samt minnesanvändning vid multipel instansiering mättes för att analyseras och jämföras. Resultat – Intermittenta svarstids-toppar gjorde datan från unikernels svår att korrekt utvärdera. Containrar hade ett lägre medelvärde på svarstider vid mindre krävande algoritm-användning. Unikernels medelvärde dyker under container-svarstiderna när mer krävande program simuleras. Några små buggar upptäcktesi Unikraft unikernels. Implikationer – Unikernels har egenskaper som gör de mer passande för vissa uppgifter jämfört med dess motsvarighet medan detsamma gäller för Containrar. Unikraft unikernels är instabila och ger därfören bild av att containrar vidmindre processorkrävande program faktiskt är snabbare än unikernels. Unikernels är bara snabbare och säkrare i den mån verktyget som bygger dem, gör det på ett sätt att de är stabila. Begränsningar – Avsaknaden av standarder, avsaknaden av ett communitysom kan svara på frågor tillsammans med att unikernelsär ett litet och nischat fält gör att unikernels har en relativ hög inlärningskurva. Nyckelord – Unikernel, Unikraft, Container, Docker
Durelli, Vinicius Humberto Serapilha. "Toward harnessing a Java high-level language virtual machine for supporting software testing." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-06012014-150025/.
Full textMáquinas virtuais de linguagens de programação têm desempenhado um papel importante como mecanismo para a implementação de linguagens de programação. Linguagens voltadas para esses ambientes de execução possuem várias vantagens em relação às linguagens compiladas. Essas vantagens fizeram com que tais ambientes de execução se tornassem amplamente utilizados pela indústria e academia. Entretanto, a maioria dos estudos nessa area têm se dedicado a aprimorar o desempenho desses ambientes de execução e poucos têm enfocado o desenvolvimento de funcionalidades que automatizem ou facilitem a condução de atividades de engenharia de software, incluindo atividades de teste de software. Este trabalho apresenta indícios de que máquinas virtuais de linguagens de programação podem apoiar a criação de ambientes de teste de software integrado. Para tal, duas funcionalidades que tiram proveito das características de uma máquina virtual Java foram desenvolvidas. O propósito da primeira funcionalidade e automatizar a condução de atividades de mutação fraca. Após a implementação de tal funcionalidade na máquina virtual Java selecionada, observou-se um desempenho até 95% melhor em relação a uma ferramenta de mutação forte. Afim de apoiar o teste de programas concorrentes, a segunda funcionalidade permite reexecutá-los de forma determinística além de automatizar a exploração de que novas sequências de escalonamento
Mohammad, Taha, and Chandra Sekhar Eati. "A Performance Study of VM Live Migration over the WAN." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1529.
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Books on the topic "Virtual Machines (VM)"
Corporation, International Business Machines, ed. Conversion guide and notebook for VM/XA SP and VM/ESA, release 2.2: Virtual machine/enterprise systems architecture. 5th ed. Endicott, NY (1701 North St., Endicott 13760-5553): International Business Machines Corp., 1994.
Find full textAbreu, Peter, and Paul Olenick. Windows Azure Virtual Machines: Deploy and Run Windows Server or Linux VM. Wiley & Sons, Incorporated, John, 2013.
Find full textBook chapters on the topic "Virtual Machines (VM)"
Yuan, Shenghao, Frédéric Besson, Jean-Pierre Talpin, Samuel Hym, Koen Zandberg, and Emmanuel Baccelli. "End-to-End Mechanized Proof of an eBPF Virtual Machine for Micro-controllers." In Computer Aided Verification, 293–316. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_15.
Full textChoi, Brendan. "Creating an Ubuntu Server Virtual Machine (VM)." In Introduction to Python Network Automation Volume I - Laying the Groundwork, 271–309. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0146-4_5.
Full textChoi, Brendan. "Creating a Fedora Server Virtual Machine (VM)." In Introduction to Python Network Automation Volume I - Laying the Groundwork, 311–50. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0146-4_6.
Full textGuo, Feng, Dong Zhang, Zhengwei Liu, and Kaiyuan Qi. "VM $$^3$$ : Virtual Machine Multicast Migration Based on Comprehensive Load Forecasting." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 66–75. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16050-4_6.
Full textKwon, Donghyun, Jiwon Seo, Sehyun Baek, Giyeol Kim, Sunwoo Ahn, and Yunheung Paek. "VM-CFI: Control-Flow Integrity for Virtual Machine Kernel Using Intel PT." In Computational Science and Its Applications – ICCSA 2018, 127–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95174-4_10.
Full textHussain, Mohammad Rashid, Arshi Naim, and Mohammed Abdul Khaleel. "Implementation of Wireless Sensor Network Using Virtual Machine (VM) for Insect Monitoring." In Lecture Notes in Networks and Systems, 73–78. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3172-9_8.
Full textPadhi, Biswajit, Motahar Reza, Indrajeet Gupta, Poorna Sai Nagendra, and Sarath S. Kumar. "Prediction of Dynamic Virtual Machine (VM) Provisioning in Cloud Computing Using Deep Learning." In Computational Intelligence in Data Mining, 607–18. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9447-9_46.
Full textAlmamun, Shawakat Akbar, E. Balamurugan, Shahidul Hasan, N. M. Saravana Kumar, and K. Sangeetha. "Intelligent Stackelberg Game Theory with Threshold-Based VM Allocation Strategy for Detecting Malicious Co-Resident Virtual Nodes in Cloud Computing Networks." In Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems, 249–67. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003107477-14.
Full textSharma, Oshin, and Hemraj Saini. "Performance Evaluation of Energy-Aware Virtual Machine Placement Techniques for Cloud Environment." In Advances in Human Resources Management and Organizational Development, 45–72. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5323-6.ch003.
Full text"Modularity Design of VM." In Advanced Design and Implementation of Virtual Machines, 229–42. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315386706-17.
Full textConference papers on the topic "Virtual Machines (VM)"
Amamou, Ahmed, Manel Bourguiba, Kamel Haddadou, and Guy Pujolle. "DBA-VM: Dynamic bandwidth allocator for virtual machines." In 2012 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2012. http://dx.doi.org/10.1109/iscc.2012.6249382.
Full textDa Silva, Rodrigo A. C., and Nelson L. S. Da Fonseca. "Energy-aware load balancing in distributed data centers." In XXIX Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2016.9133.
Full textLi, Nan, Bo Li, Jianxin Li, Tianyu Wo, and Jinpeng Huai. "vMON: An Efficient Out-of-VM Process Monitor for Virtual Machines." In 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2013. http://dx.doi.org/10.1109/hpcc.and.euc.2013.194.
Full textAdeshara, Nandan, Ajinkya Rede, Suhani Jain, Krishna Dhoot, and Sunil Mhamane. "Optimizing Resource Utilization by Vm Migration Among Virtual Machines of a Cloud Server." In 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020. http://dx.doi.org/10.1109/icces48766.2020.9138010.
Full textLi, Yaqiong, and Yongbing Huang. "TMemCanal: A VM-oblivious Dynamic Memory Optimization Scheme for Virtual Machines in Cloud Computing." In 2010 IEEE 10th International Conference on Computer and Information Technology (CIT). IEEE, 2010. http://dx.doi.org/10.1109/cit.2010.68.
Full textBraun, Tom, Marcel Taeumel, Eliot Miranda, and Robert Hirschfeld. "Transpiling Slang Methods to C Functions: An Example of Static Polymorphism for Smalltalk VM Objects." In VMIL '23: 15th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3623507.3623548.
Full textJaison, Feon, and Gulista Khan. "Migration and Scheduling of Virtual Machines (VM) using Priority Based Load Balancing in Cloud Environment." In 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2023. http://dx.doi.org/10.1109/icaiccit60255.2023.10465709.
Full textLange, Adriano, Marcos Sunye, and Luis Carlos Bona. "Upstream: Exposing Performance Information from Cloud Providers to Tenants." In XX Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2019. http://dx.doi.org/10.5753/wscad.2019.8673.
Full textSheng, Junjie, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, Wenli Zhou, et al. "VMAgent: A Practical Virtual Machine Scheduling Platform." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/860.
Full textLuo, Chuan, Bo Qiao, Xin Chen, Pu Zhao, Randolph Yao, Hongyu Zhang, Wei Wu, Andrew Zhou, and Qingwei Lin. "Intelligent Virtual Machine Provisioning in Cloud Computing." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/208.
Full textReports on the topic "Virtual Machines (VM)"
Oleksiuk, Vasyl P., Olesia R. Oleksiuk, Oleg M. Spirin, Nadiia R. Balyk, and Yaroslav P. Vasylenko. Some experience in maintenance of an academic cloud. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4436.
Full textMcGarrigle, Malachy. Watchpoints for Consideration When Utilising a VDI Network to Teach Archicad BIM Software Within an Educational Programme. Unitec ePress, October 2023. http://dx.doi.org/10.34074/ocds.099.
Full textChandramouli, Ramaswamy. Secure Virtual Network Configuration for Virtual Machine (VM) Protection. National Institute of Standards and Technology, March 2016. http://dx.doi.org/10.6028/nist.sp.800-125b.
Full textYu, Ken F. Android Virtual Machine (VM) Setup on Linux. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada612920.
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