efficient virtual machine migration-会议论文survey
人工智能英文参考文献(最新120个)
人工智能是一门新兴的具有挑战力的学科。
自人工智能诞生以来,发展迅速,产生了许多分支。
诸如强化学习、模拟环境、智能硬件、机器学习等。
但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。
下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。
人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? [J]. ,2020,25(4).[47]Savage Rock H,van Assen Marly,Martin Simon S,Sahbaee Pooyan,Griffith Lewis P,Giovagnoli Dante,Sperl Jonathan I,Hopfgartner Christian,K?rgel Rainer,Schoepf U Joseph. Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[48]Brzezicki Maksymilian A,Bridger Nicholas E,Kobeti? Matthew D,Ostrowski Maciej,Grabowski Waldemar,Gill Simran S,Neumann Sandra. Artificial intelligence outperforms human students in conducting neurosurgical audits.[J]. Clinical neurology and neurosurgery,2020,192.[49]Lockhart Mark E,Smith Andrew D. Fatty Liver Disease: Artificial Intelligence Takes on the Challenge.[J]. Radiology,2020,295(2).[50]Wood Edward H,Korot Edward,Storey Philip P,Muscat Stephanie,Williams George A,Drenser Kimberly A. The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence.[J]. Current opinion in ophthalmology,2020,31(3).[51]Ho Dean,Quake Stephen R,McCabe Edward R B,Chng Wee Joo,Chow Edward K,Ding Xianting,Gelb Bruce D,Ginsburg Geoffrey S,Hassenstab Jason,Ho Chih-Ming,Mobley William C,Nolan Garry P,Rosen Steven T,Tan Patrick,Yen Yun,Zarrinpar Ali. Enabling Technologies for Personalized and Precision Medicine.[J]. Trends in biotechnology,2020,38(5).[52]Fischer Andreas M,Varga-Szemes Akos,van Assen Marly,Griffith L Parkwood,Sahbaee Pooyan,Sperl Jonathan I,Nance John W,Schoepf U Joseph. Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.[J]. AJR. American journal ofroentgenology,2020,214(5).[53]Moore William,Ko Jane,Gozansky Elliott. Artificial Intelligence Pertaining to Cardiothoracic Imaging and Patient Care: Beyond Image Interpretation.[J]. Journal of thoracic imaging,2020,35(3).[54]Hwang Eui Jin,Park Chang Min. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. Cardiovascular research,2020,116(6).[59]Fischer Andreas M,Eid Marwen,De Cecco Carlo N,Gulsun Mehmet A,van Assen Marly,Nance John W,Sahbaee Pooyan,De Santis Domenico,Bauer Maximilian J,Jacobs Brian E,Varga-Szemes Akos,Kabakus Ismail M,Sharma Puneet,Jackson Logan J,Schoepf U Joseph. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[60]Ghosh Adarsh,Kandasamy Devasenathipathy. Interpretable Artificial Intelligence: Why and When.[J]. AJR. American journal of roentgenology,2020,214(5).[61]M.Rosario González-Rodríguez,M.Carmen Díaz-Fernández,Carmen Pacheco Gómez. Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions[J]. Telematics and Informatics,2020,51.[62]Ru-Xi Ding,Iván Palomares,Xueqing Wang,Guo-Rui Yang,Bingsheng Liu,Yucheng Dong,Enrique Herrera-Viedma,Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective[J]. Information Fusion,2020,59.[63]Abdulrhman H. Al-Jebrni,Brendan Chwyl,Xiao Yu Wang,Alexander Wong,Bechara J. Saab. AI-enabled remote and objective quantification of stress at scale[J]. Biomedical Signal Processing and Control,2020,59.[64]Gillian Thomas,Elizabeth Eisenhauer,Robert G. Bristow,Cai Grau,Coen Hurkmans,Piet Ost,Matthias Guckenberger,Eric Deutsch,Denis Lacombe,Damien C. Weber. The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report[J]. European Journal of Cancer,2020,131.[65]Muhammad Asif. Are QM models aligned with Industry 4.0? A perspective on current practices[J]. Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. Shlobin,Kartik Kesavabhotla,Zachary A Smith,Nader S Dahdaleh. Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions[J]. Clinical Neurology and Neurosurgery,2020,192.[71]Mei Yang,Runze Zhou,Xiangjun Qiu,Xiangfei Feng,Jian Sun,Qunshan Wang,Qiufen Lu,Pengpai Zhang,Bo Liu,Wei Li,Mu Chen,Yan Zhao,Binfeng Mo,Xin Zhou,Xi Zhang,Yingxue Hua,Jin Guo,Fangfang Bi,Yajun Cao,Feng Ling,Shengming Shi,Yi-Gang Li. Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals[J]. Environment International,2020,139.[72]Fatemehalsadat Madaeni,Rachid Lhissou,Karem Chokmani,Sebastien Raymond,Yves Gauthier. Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review[J]. Cold Regions Science and Technology,2020,174.[73]Steve Chukwuebuka Arum,David Grace,Paul Daniel Mitchell. A review of wireless communication using high-altitude platforms for extended coverage and capacity[J]. Computer Communications,2020,157.[74]Yong-Hong Kuo,Nicholas B. Chan,Janny M.Y. Leung,Helen Meng,Anthony Man-Cho So,Kelvin K.F. Tsoi,Colin A. Graham. An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department[J]. International Journal of Medical Informatics,2020,139.[75]Matteo Terzi,Gian Antonio Susto,Pratik Chaudhari. Directional adversarial training for cost sensitive deep learning classification applications[J]. Engineering Applications of Artificial Intelligence,2020,91.[76]Arman Kilic. Artificial Intelligence and Machine Learning in Cardiovascular Health Care[J]. The Annals of Thoracic Surgery,2020,109(5).[77]Hossein Azarmdel,Ahmad Jahanbakhshi,Seyed Saeid Mohtasebi,Alfredo Rosado Mu?oz. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)[J]. Postharvest Biology and Technology,2020,166.[78]Wafaa Wardah,Abdollah Dehzangi,Ghazaleh Taherzadeh,Mahmood A. Rashid,M.G.M. Khan,Tatsuhiko Tsunoda,Alok Sharma. Predicting protein-peptide binding sites with a deep convolutional neural network[J]. Journal of Theoretical Biology,2020,496.[79]Francisco F.X. Vasconcelos,Róger M. Sarmento,Pedro P. Rebou?as Filho,Victor Hugo C. de Albuquerque. Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis[J]. Engineering Applications of Artificial Intelligence,2020,91.[80]Masaaki Konishi. Bioethanol production estimated from volatile compositions in hydrolysates of lignocellulosic biomass by deep learning[J]. Journal of Bioscience and Bioengineering,2020,129(6).人工智能英文参考文献三：[81]J. Kwon,K. Kim. Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography[J]. Journal of Heart and Lung Transplantation,2020,39(4).[82]C. Maathuis,W. Pieters,J. van den Berg. Decision support model for effects estimation and proportionality assessment for targeting in cyber operations[J]. Defence Technology,2020.[83]Samer Ellahham. Artificial Intelligence in Diabetes Care[J]. The American Journal of Medicine,2020.[84]Yi-Ting Hsieh,Lee-Ming Chuang,Yi-Der Jiang,Tien-Jyun Chang,Chung-May Yang,Chang-Hao Yang,Li-Wei Chan,Tzu-Yun Kao,Ta-Ching Chen,Hsuan-Chieh Lin,Chin-Han Tsai,Mingke Chen. Application of deep learning image assessment software VeriSee? for diabetic retinopathy screening[J]. Journal of the Formosan Medical Association,2020.[85]Emre ARTUN,Burak KULGA. Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference[J]. Petroleum Exploration and Development Online,2020,47(2).[86]Alberto Arenal,Cristina Armu?a,Claudio Feijoo,Sergio Ramos,Zimu Xu,Ana Moreno. Innovation ecosystems theory revisited: The case of artificial intelligence in China[J]. Telecommunications Policy,2020.[87]T. Som,M. Dwivedi,C. Dubey,A. Sharma. Parametric Studies on Artificial Intelligence Techniques for Battery SOC Management and Optimization of Renewable Power[J]. Procedia Computer Science,2020,167.[88]Bushra Kidwai,Nadesh RK. Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems[J]. Procedia Computer Science,2020,167.[89]Asl? Bozda?,Ye?im Dokuz,?znur Begüm G?k?ek. Spatial prediction of PM 10 concentration using machine learning algorithms in Ankara, Turkey[J]. Environmental Pollution,2020.[90]K.P. Smith,J.E. Kirby. Image analysis and artificial intelligence in infectious disease diagnostics[J]. Clinical Microbiology and Infection,2020.[91]Alklih Mohamad YOUSEF,Ghahfarokhi Payam KAVOUSI,Marwan ALNUAIMI,Yara ALATRACH. Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East[J]. Petroleum Exploration and Development Online,2020,47(2).[92]Omer F. Ahmad,Danail Stoyanov,Laurence B. Lovat. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[93]Sanne A. Hoogenboom,Ulas Bagci,Michael B. Wallace. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[94]Douglas K. Rex. Can we do resect and discard with artificial intelligence-assisted colon polyp “optical biopsy?”[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[95]Neal Shahidi,Michael J. Bourke. Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[96]Michael Byrne. Artificial intelligence in gastroenterology[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[97]Piet C. de Groen. Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[98]Robin Zachariah,Andrew Ninh,William Karnes. Artificial intelligence for colon polyp detection: Why should we embrace this?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[99]Alexandra T. Greenhill,Bethany R. Edmunds. A primer of artificial intelligence in medicine[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[100]Tomohiro Tada,Toshiaki Hirasawa,Toshiyuki Yoshio. The role for artificial intelligence in evaluation of upper GI cancer[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[101]Yahui Jiang,Meng Yang,Shuhao Wang,Xiangchun Li,Yan Sun. Emerging role of deep learning‐based artificial intelligence in tumor pathology[J]. Cancer Communications,2020,40(4).[102]Kristopher D. Knott,Andreas Seraphim,Joao B. Augusto,Hui Xue,Liza Chacko,Nay Aung,Steffen E. Petersen,Jackie A. Cooper,Charlotte Manisty,Anish N. Bhuva,Tushar Kotecha,Christos V. Bourantas,Rhodri H. Davies,Louise A.E. Brown,Sven Plein,Marianna Fontana,Peter Kellman,James C. Moon. The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence–Based Approach Using Perfusion Mapping[J]. Circulation,2020,141(16).[103]Muhammad Asad,Ahmed Moustafa,Takayuki Ito. FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning[J]. Applied Sciences,2020,10(8).[104]Wu Wenzhi,Zhang Yan,Wang Pu,Zhang Li,Wang Guixiang,Lei Guanghui,Xiao Qiang,Cao Xiaochen,Bian Yueran,Xie Simiao,Huang Fei,Luo Na,Zhang Jingyuan,Luo Mingyan. Psychological stress of medical staffs during outbreak of COVID-19 and adjustment strategy.[J]. Journal of medical virology,2020.[105]. Eyenuk Fulfills Contract for Artificial Intelligence Grading of Retinal Images[J]. Telecomworldwire,2020.[106]Kim Tae Woo,Duhachek Adam. Artificial Intelligence and Persuasion: A Construal-Level Account.[J]. Psychological science,2020,31(4).[107]McCall Becky. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread.[J]. The Lancet. Digital health,2020,2(4).[108]Alca?iz Mariano,Chicchi Giglioli Irene A,Sirera Marian,Minissi Eleonora,Abad Luis. [Autism spectrum disorder biomarkers based on biosignals, virtual reality and artificial intelligence].[J]. Medicina,2020,80 Suppl 2.[109]Cong Lei,Feng Wanbing,Yao Zhigang,Zhou Xiaoming,Xiao Wei. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer.[J]. Journal of Cancer,2020,11(12).[110]Wang Fengdan,Gu Xiao,Chen Shi,Liu Yongliang,Shen Qing,Pan Hui,Shi Lei,Jin Zhengyu. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development.[J]. PeerJ,2020,8.[111]Hu Wenmo,Yang Huayu,Xu Haifeng,Mao Yilei. Radiomics based on artificial intelligence in liver diseases: where we are?[J]. Gastroenterology report,2020,8(2).[112]Batayneh Wafa,Abdulhay Enas,Alothman Mohammad. Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques.[J]. Heliyon,2020,6(4).[113]Aydin Emrah,Türkmen ?nan Utku,Namli G?zde,?ztürk ?i?dem,Esen Ay?e B,Eray Y Nur,Ero?lu Egemen,Akova Fatih. A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children.[J]. Pediatric surgery international,2020.[114]Ellahham Samer. Artificial Intelligence in Diabetes Care.[J]. The Americanjournal of medicine,2020.[115]David J. Winkel,Thomas J. Weikert,Hanns-Christian Breit,Guillaume Chabin,Eli Gibson,Tobias J. Heye,Dorin Comaniciu,Daniel T. Boll. Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation[J]. European Journal of Radiology,2020,126.[116]Binjie Fu,Guoshu Wang,Mingyue Wu,Wangjia Li,Yineng Zheng,Zhigang Chu,Fajin Lv. Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study[J]. European Journal of Radiology,2020,126.[117]Georgios N. Kouziokas. A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting[J]. Engineering Applications of Artificial Intelligence,2020,92.[118]Qingsong Ruan,Zilin Wang,Yaping Zhou,Dayong Lv. A new investor sentiment indicator ( ISI ) based on artificial intelligence: A powerful return predictor in China[J]. Economic Modelling,2020,88.[119]Mohamed Abdel-Basset,Weiping Ding,Laila Abdel-Fatah. The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model[J]. Information Fusion,2020,61.[120]Federico Caobelli. Artificial intelligence in medical imaging: Game over for radiologists?[J]. European Journal of Radiology,2020,126.以上就是关于人工智能参考文献的分享，希望对你有所帮助。
VMware2012演讲： 关键应用容灾 Simple and Reliable Disaster Recovery for All Virtualized Applications
• 46% of our customers
reported “Use virtualization to improve BCDR” in their top 5 objectives for virtualization
4
Source: Forrester
Recovery Needs Are Top Of IT And Data Center Initiatives
Improving BCDR capabilities is a key priority: • #1 for SMBs • #2 for Enterprises
BCDR is #1 driver for
Traditional Solutions for Disaster Recovery Are Not Adequate
Expensive
Software
Complex Recovery Plans
Apps
Unreliable Failovers
Hosts
? ?
Storage
?
Hosts
Storage
Disaster Recovery
vSphere Replication vCenter Site Recovery Manager DR to the Cloud services based on
SRM
Data Protection
vSphere Data Protection vSphere Storage APIs for Data
Features are subject to change, and must not be included in
非易失性内存友好的线性哈希索引——NVM-LH
2021⁃03⁃10计算机应用,Journal of Computer Applications 2021,41(3):623-629ISSN 1001⁃9081CODEN JYIIDU http ：//非易失性内存友好的线性哈希索引——NVM -LH汤晨1，黄国锐2，金培权1*（1.中国科学技术大学计算机科学与技术学院，合肥230001；2.中国人民解放军31002部队，北京100081）（∗通信作者电子邮箱jpq@ ）摘要：非易失性内存（NVM ）因其大容量、持久化、按位存取和读延迟低等特性而受到人们的关注，但它同时也具有写次数有限、读写速度不均衡等缺点。
针对传统线性哈希索引直接在NVM 上实现时会导致大量的随机写操作这一问题，提出了一种新的NVM 友好的线性哈希索引NVM -LH 。
NVM -LH 通过存储数据时的缓存行对齐实现了缓存友好性，同时提出了无日志的数据一致性保证策略。
此外，NVM -LH 还通过优化分裂和删除操作来减少NVM 写操作。
实验结果表明，NVM -LH 在空间利用率上比CCEH 高30%，在NVM 写次数上比CCEH 减少了15%左右，表现了更好的NVM 友好性。
关键词：非易失性内存；动态哈希；线性哈希；缓存行友好性；数据一致性中图分类号：TP392文献标志码：ANVM -LH ：non -volatile memory -friendly linear hash indexTANG Chen 1，HUANG Guorui 2，JIN Peiquan 1*（1.School of Computer Science and Technology ，University of Science and Technology of China ，Hefei Anhui 230001，China ；2.Unit 31002，Chinese People s Liberation Army ，Beijing 100081，China ）Abstract:Non -Volatile Memory （NVM ）attracts people s attention because of its large capacity ，persistence ，bitaddressability and low read latency.However ，it also has some disadvantages ，such as limited writes and asymmetric readingand writing speed.When the traditional linear hash index is implemented directly on NVM ，it will lead to a great number of random write operations.To solve this problem ，a new NVM -friendly linear hash index called NVM -LH （NVM -oriented Linear Hashing ）was proposed.The cache friendliness was achieved by NVM -LH through the cache line alignment duringstoring data.And a log -free data consistency guaranteeing strategy was presented in NVM -LH.In addition ，the split and delete operations were optimized in NVM -LH to minimize the NVM write operations.Experimental results show that NVM -LH outperforms the state -of -the -art NVM -aware hash index CCEH （Cacheline -Conscious Extendible Hashing ）in terms ofspace utilization （30%higher ）and NVM write number （about 15%lower ），showing better NVM -friendliness.Key words:Non -Volatile Memory (NVM);dynamic hashing;linear hashing;cache line friendliness;data consistency引言在过去的数十年中，由于存储密度的限制，动态随机访问内存（Dynamic Random Access Memory ，DRAM ）的容量始终无法超越64GB ，不能满足大数据应用对大容量内存的需求。
一种迁移开销感知的虚拟机动态整合算法
一种迁移开销感知的虚拟机动态整合算法张欢;李仁发;黄晶【期刊名称】《计算机工程与应用》【年(卷),期】2016(052)021【摘要】The existing dynamic consolidation of virtual machines algorithm which aims to reduce energy consumption does not consider the cost of the migration of virtual machines. Some migration of virtual machine is unreasonable, which probably increases the SLA(Service Level Agreements)violation. The results in dynamic consolidation of virtual machine decrease the energy consumption of data center. To address this problem, this paper proposes a migration cost-aware dynamic consolidation of virtual machine algorithm called MigCAP(Migration Cost Aware Policy). It defines the migra-tion profile parameter EMP and through this parameter, MigCAP chooses to migrate the virtual machines or not. The experimental results show that MigCAP method reduces not only energy consumption and SLA violation but also the num-ber of migrations of virtual machines.%现有的以降低能耗为目标的虚拟机动态整合算法通常忽略了虚拟机迁移所带来的消极影响，导致虚拟机的动态整合虽然减少了数据中心的能耗，但不合理的虚拟机迁移次数较多，极有可能增加了SLA（Service Level Agreements）的违例率。
吴恩达2篇
吴恩达2篇吴恩达是计算机科学家、人工智能专家，也是深度学习领域的奠基人之一。
在他的职业生涯中，他做出了许多重要的贡献，推动了人工智能的发展。
本篇文章将分别介绍吴恩达的两篇经典论文，分别是《Unsupervised Feature Learning and Deep Learning》和《Deep Learning of Representations for Unsupervised and Transfer Learning》。
第一篇论文《Unsupervised Feature Learning and Deep Learning》是吴恩达在2012年发表的。
这篇论文主要介绍了无监督特征学习和深度学习的方法，并提出了一种新的算法Deep Belief Networks（DBNs）。
DBNs是一种无监督学习的神经网络，通过多层次的学习来学习特征表示。
本篇论文首先介绍了传统的无监督特征学习方法，如自编码器和K-means等。
然后，吴恩达介绍了DBNs的工作原理和训练过程。
DBNs 由多个Restricted Boltzmann Machines（RBMs）组成，每个RBMs都学习到一组特征，并将其传递给上一层。
通过逐层的学习，DBNs能够学习到更高级别的特征表示，从而提取数据中的有用信息。
吴恩达还介绍了DBNs在图像分类和语音识别等任务中的应用，并展示了其与传统方法相比的优势。
通过无监督学习的特征表示，DBNs能够更好地处理大规模数据和复杂问题，并取得更好的性能。
第二篇论文《Deep Learning of Representations for Unsupervised and Transfer Learning》是吴恩达在2013年发表的。
这篇论文进一步探讨了深度学习的特征表示和迁移学习的问题。
吴恩达认为，深度学习的关键在于学习到具有高度可区分性和泛化能力的特征表示。
本篇论文首先介绍了深度学习中的监督学习和无监督学习。
efficient_multi-scale_attention_module_概述及解释说明
efficient multi-scale attention module 概述及解释说明1. 引言1.1 概述本篇文章将介绍“efficient multi-scale attention module（高效多尺度注意力模块）”的概念和解释。
该模块是一种用于计算机视觉领域的新型技术，旨在提升在多尺度场景下的特征提取和表征能力。
本文将详细阐述该模块的定义、应用场景以及优势。
1.2 文章结构本文共分为五个部分：引言、efficient multi-scale attention module概述、解释说明efficient multi-scale attention module的关键要点、其他相关研究工作概述和比较分析以及结论。
通过这样的结构，读者能够全面了解并深入探索efficient multi-scale attention module的概念和其在计算机视觉领域中的重要性。
1.3 目的本文旨在向读者介绍efficient multi-scale attention module的基本原理、设计思路以及其在实践中所展现出来的优越性。
通过对其关键要点进行详细解释和说明，希望读者能够对该模块有更加清晰全面的理解，并认识到其在计算机视觉领域中所具有的广泛应用前景和重要意义。
此外，通过与其他相关研究工作的比较分析，读者将能够更好地理解efficient multi-scale attention module与传统注意力机制以及现有多尺度模型之间的差异与优势所在。
通过对未来发展方向和应用领域进行展望，并回顾整篇文章的主要内容，我们希望本文能够为读者提供一个全面深入了解efficient multi-scale attention module的参考，并为相关领域研究提供有益启示。
2. efficient multi-scale attention module 概述:2.1 多尺度注意力机制简介:在计算机视觉和深度学习领域，多尺度注意力机制被广泛应用于图像和视频处理任务中。
StarWind Virtual SAN HyperConverged 2-Node 技术文档说明书
One Stop Virtualization Shop StarWind Virtual SAN®HyperConverged 2-Node Scenario with VMware vSphereAPRIL 2018TECHNICAL PAPERTrademarks“StarWind”,“StarWind Software” and the StarWind and the StarWind Software logos are registered trademarks of StarWind Software. “StarWind LSFS” is a trademark of StarWind Software which may be registered in some jurisdictions. All other trademarks are owned by their respective owners.ChangesThe material in this document is for information only and is subject to change without notice. While reasonable efforts have been made in the preparation of this document to assure its accuracy, StarWind Software assumes no liability resulting from errors or omissions in this document, or from the use of the information contained herein. StarWind Software reserves the right to make changes in the product design without reservation and without notification to its users.Technical Support and ServicesIf you have questions about installing or using this software, check this and other documents first - you will find answers to most of your questions on the Technical Papers webpage or in StarWind Forum. If you need further assistance, please contact us.About StarWindStarWind is a pioneer in virtualization and a company that participated in the development of this technology from its earliest days. Now the company is among the leading vendors of software and hardware hyperconverged solutions. The company’s core product is the years-proven StarWind Virtual SAN, which allows SMB and ROBO to benefit from cost-efficient hyperconverged IT infrastructure. Having earned a reputation of reliability, StarWind created a hardware product line and is actively tapping into hyperconverged and storage appliances market. In 2016, Gartner named StarWind “Cool Vendor for Compute Platforms” following the success and popularity of StarWind HyperConverged Appliance. StarWind partners with world-known companies: Microsoft, VMware, Veeam, Intel, Dell, Mellanox, Citrix, Western Digital, etc.Copyright ©2009-2018 StarWind Software Inc.No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written consent of StarWind Software.ContentsIntroduction 4 Pre-Configuring Servers 5 Preparing Hypervisor for StarWind Deployment 6 Configuring Networks 6 Preparing StarWind VMs 8 Configuring StarWind VMs startup/shutdown 9 Downloading, Installing and Registering the Software 10 Configuring Automatic storage rescan 17 Provisioning Storage with StarWind VSAN 25 Creating StarWind HA devices (DS1, DS2) 25 Preparing Datastores 34 Adding Discover portals 34 Creating Datastores 37 Additional tweaks 44 Creating a Datacenter 48 Creating a Cluster 49 Adding Hosts to the Cluster 50 Turn on the vSphere HA Feature 54 Conclusion 55IntroductionTraditionally, VMware vSphere requires a shared storage to ensure data safety, allow virtual machine migration, enable continuous application availability, and eliminate any single point of failure within an IT environment. VMware vSphere users need to choose between two options when selecting the shared storage:• Hyperconverged solutions, which allow sharing the same hardware resources for the application (i.e., hypervisor, database) and the shared storage, thusdecreasing the TCO and achieving the outstanding performance results.• Compute and Storage separated solutions, which keep the compute and storage layers separate from each other, thus making the maintenance easier, increasing the hardware utilization efficiency, and allowing building the system for thespecific purposeThis guide is intended for experienced VMware and Windows system administrators and IT professionals who need to configure StarWind Virtual SAN® hyperconverged solution for vSphere deployments. It provides step-by-step guidance on configuring a 2-node vSphere cluster using StarWind Virtual SAN® to convert the local storage of the ESXi hosts into a fault-tolerant shared storage resource for ESXi.A full set of the up-to-date technical documentation can always be found here, or by pressing the Help button in the StarWind Management Console.For any technical inquiries, please, visit our online community, Frequently Asked Questions page, or use the support form to contact our technical support department.Pre-Configuring ServersThe diagram below illustrates the network and storage configuration of the solution described in this guide.Preparing Hypervisor for StarWind Deployment Configuring NetworksConfigure network interfaces on each node to make sure that Synchronization andiSCSI/StarWind heartbeat interfaces are in different subnets and connected physically according to the network diagram above. In this document, 172.16.10.x subnet is used for iSCSI/StarWind heartbeat traffic, while 172.16.20.x one is used for the Synchronization traffic. All actions below should be applied to each ESXi server.1.Create a vSwitch to use for Management traffic if it is not presented.2.Create a vSwitch for the iSCSI/ StarWind Heartbeat channel.NOTE: A Virtual Machine Port Group should be created for iSCSI/ StarWind Heartbeat and Synchronization vSwitches, while a VMKernel port should be created only for iSCSI traffic. Static IP address should be assigned to VMKernel ports.3.Create a vSwitch for the Synchronization channel.NOTE: It is recommended to set jumbo frames to 9000 on vSwitches and VMKernel ports for iSCSI and Synchronization traffic. Additionally, vMotion can be enabled on VMKernel ports.4.Repeat the steps above for any other links intended for Synchronization andiSCSI/Heartbeat traffic on both ESXi nodes.Preparing StarWind VMs5.Create Virtual Machines (VMs) on each Windows Server 2016/2012R2 ESXi host () andinstall StarWind VSAN.StarWind VMs on ESXi hosts should be configured with the following settings:RAM: at least 4 GB (plus the size of the RAM cache if it is going to be used);CPUs: at least 4 virtual processors with 2 GHz reserved;Hard disk 1: 100 GB for OS (recommended);Hard disk 2: Depends on the storage volume intended for the shared storage.NOTE: Each hard disk should be Thick Provisioned Eager Zeroed.Network adapter 1: ManagementNetwork adapter 2: iSCSINetwork adapter 3: SyncNOTE: All network adapters should be VMXNET3.NOTE: Active Directory Domain Services role can be added on StarWind Virtual Machine (VM) if necessar so that it could serve as a domain controller.Configuring StarWind VMs startup/shutdown6.Set up the VMs startup policy on both ESXi hosts in Configuration -> Virtual MachineStartup and Shutdown -> Properties menu. In the window that pops up, check Allow virtual machines to start and stop automatically with the system to enable the option, choose the stop-action as Guest Shutdown, and move up the StarWind VMs.Click OK to proceed.7.Start both StarWind virtual machines, install OS and StarWind Virtual SAN.Downloading, Installing and Registering the Software8.Download the StarWind setup executable file from the official website by following thelink:https:///registration-starwind-virtual-sanunch the downloaded setup file on the server where StarWind Virtual SAN or one ofits components is to be installed. The setup wizard appears.10.Read and accept the License Agreement.Click Next to continue.11.Read the information about the new features and improvements carefully. Red textindicates warnings for users who are updating their existing software installations.Click Next to continue.12.Select the following components for the minimum setup:•StarWind Virtual SAN ServiceStarWind V SAN service is the “core” of the software. It allows creating iSCSI targets andsharing virtual and physical devices. The service can be managed via StarWindManagement Console on any Windows computer or VSA connected to the network.Alternatively, the service can be managed from StarWind Web Console which is deployed separately.•StarWind Management ConsoleStarWind Management Console is the Graphic User Interface (GUI) part of the software that controls and monitors all storage-related operations (e.g., allows users to createtargets and devices on StarWind Virtual SAN servers connected to the network).13.Specify Start Menu Folder.Click Next to continue.14.Tick the checkbox if a desktop icon is needed.15.The prompt that appears allows requesting a time-limited fully functional evaluation keyor a FREE version key. Alternatively, use the already purchased StarWind Virtual SAN commercial license key. Select the corresponding option.16.Click Browse to choose the license file.17.Verify the licensing information.Click Next to apply the license key.18.Verify the installation settings. Click Back to make any changes or Install to continue.19.Click Finish to complete the installation process. Optionally, StarWind ManagementConsole can be launched by ticking the corresponding checkbox.20.Repeat the steps above on the second virtual machine.Configuring Automatic storage rescanFor each ESXi host, configure the automatic storage rescan.Log in to StarWind VM and install vSphere PowerCLI on each StarWind virtual machine by adding the PowerShell module (Internet connectivity is required). To do so, run the following PowerShell command:Install-Module-Name VMware.PowerCLI-AllowClobberNOTE: In case of using Windows Server 2012 R2, online PowerCLI installation requires Windows Management Framework 5.1 or later installed on VMs. Windows Management Framework 5.1 can be downloaded by following the link:https:///fwlink/?linkid=83951621.Open PowerShell and change the Execution Policy to Unrestricted by running thefollowing command:Set-ExecutionPolicy Unrestricted22.Create PowerShell script which will perform an HBA rescan on the hypervisor host.Import-Module VMware.PowerCLI$counter=1if ($counter-eq0){Set-PowerCLIConfiguration-InvalidCertificateAction ignore-Confirm:$false|Out-Null}$ESXiHost="IP address"$ESXiUser="Login"$ESXiPassword="Password"Connect-VIServer$ESXiHost-User$ESXiUser-Password$ESXiPassword|Out-NullGet-VMHostStorage$ESXiHost-RescanAllHba|Out-NullGet-ScsiLun-VMHost$ESXiHost-LunType disk|Where-Object Vendor-EQ"STARWIND"|Where-Object ConsoleDeviceName-NE" "|Set-ScsiLun-MultipathPolicy RoundRobin|Out-Null$StarwindCN=Get-ScsiLun-VMHost$ESXiHost-LunType disk|Where-Object Vendor-EQ"STARWIND"|Where-Object ConsoleDeviceName-NE" "|Select-Object CanonicalName$esxcli=Get-EsxCli-VMHost$ESXiHostforeach($CN in$StarwindCN){$esxcli.storage.nmp.psp.roundrobin.deviceconfig.set(0,$null,$CN.CanonicalName,1," iops",0) |Out-Null}Disconnect-VIServer$ESXiHost-Confirm:$false$file=Get-Content"$PSScriptRoot\rescan_script.ps1"if ($file[1]-ne"`$counter = 1") {$file[1]="`$counter = 1"$file>"$PSScriptRoot\rescan_script.ps1"}In the corresponding lines, specify the IP address and login credentials of the ESXi host on which the current StarWind VM is stored and running:$ESXiHost1 = “IP address”$ESXiUser = “Login”$ESXiPassword = “Password”Save the script as rescan_script.ps1 to the root of the C:\ drive of the VM.23.Perform the configuration steps above on the partner node.24.Go to Control Panel -> Administrative Tools -> Task Scheduler -> Create BasicTask and follow the wizard steps:25.Specify the task name, select When a specific event is logged, and click Next.26.Select Application in the Log dropdown menu, type StarWindService as the eventsource and 788 as the event ID. Click the Next button.27.Choose Start a Program as an action the task should perform and click Next.28.Type powershell.exe in the Program/script field. In the Add arguments field, type:“ -ExecutionPolicy Bypass -NoLogo -NonInteractive -NoProfile -WindowStyle Hidden -File C:\rescan_script.ps1 ”Click the Next button to continue.29.Click Finish to exit the wizard.30.Configure the task to run with the highest privileges by tickingthe correspondingcheckbox in the window. Make sure that the “Run whether user is logged on or not”option is selected.31.Switch to the Triggers tab. Verify that the trigger on event 788 is set up correctly.32.Click New and add other triggers by Event ID 782, 257, 773, and 817.33.All added triggers should look like in the screenshot below.34.Switch to the Actions tab and verify the parameters for the task.Press OK and type the credentials for the user whose rights are used to execute the command in the prompt.35.Perform the same steps on the second StarWind VM, specifying the correspondingsettings.Provisioning Storage with StarWind VSANCreating StarWind HA devices (DS1, DS2)36.Open StarWind Management Console and click the Add Device (advanced) button.37.Select Hard disk device as the type of the created device. Click Next to continue.38.Select Virtual disk. Click Next to continue.39.Specify Virtual Disk Location and Size.Click Next.40.Specify Virtual Disk Options and click Next to continue.NOTE:For ESXi, use 512 bytes Block Size41.Define the RAM caching policy and specify the cache size (in corresponding units) andclick Next to continue.NOTE:The recommended RAM cache size is 1 GB per 1 TB of storage.42.Define the flash caching policy and the cache size. Click Next to continue.NOTE:The recommended flash cache size is 10% of the created device size.43.Specify the target parameters. Select the Target Name checkbox to enter a customtarget name if required. Otherwise, the name will be generated automatically inaccordance with the specified target alias.Click Next to continue.44.Click Create to add a new device and attach it to the target.45.Click Finish to close the wizard.46.Right-click the recently created device and select Replication Manager from theshortcut menu.47.Click Add replica.48.Select Synchronous two-way replication as a replication mode and click Next toproceed.49.Specify Partner Host Name, IP Address, and Port Number.Click Next.50.Choose Create new Partner Device and click Next.51.Choose the device Location and specify Target Name if required. Otherwise, the namewill be generated automatically according to the specified target alias.52.Click Change Network Settings.53.Specify Interfaces for Synchronization and Heartbeat channels.Click OK. Then click Next.54.Choose Synchronize from existing Device55.Click Create Replica.Click Finish to close the wizard.56.The successfully added devices appear in StarWind Management Console.57.Repeat the steps described above to create other virtual disks that will be used asdatastores.Preparing DatastoresAdding Discovery portals58.To connect the previously created devices to ESX host, navigate to the Configurationtab, then click on Storage Adapters and choose (or add) the ISCSI storage adapter. In the Details section, click the Properties tab.59.In Dynamic Discovery click the Add… button to specify iSCSI interfaces.60.Enter the iSCSI IP address of the first StarWind node from the virtual local network172.16.10.*Click OK.61.Add the IP address of the second StarWind node – 172.16.10.*Click OK.62.Everything should look like the image below.63.Rescan the storage.64.Now, the previously created StarWind devices are visible.65.Repeat all the steps from this section for the other ESXi node, specifying thecorresponding IP addresses for the iSCSI subnet.Creating Datastores66.Open Configuration tab of any host and click Storage.67.Click Add Storage.68.Select Disk/LUN.69.Select the previously discovered StarWind device and click Next.70.Check Current Disk Layout and click Next.71.Specify the datastore name and click Next.72.Enter the datastore size and click Next.73.Verify the settings and click Finish.74.Add another Datastore (DS2) as it is described above but select the second device forthat purpose.75.Verify that the storages (DS1, DS2) are connected to both hosts. Otherwise, rescan thestorage adapter.76.Right-clickon a datastore and select Properties.77.Click Manage Paths.78.Change the Path Selection policy to the Round Robin one and click Change.79.Repeat the same steps for each Datastore for each host.Additional tweaks80.Click on the configuration tab of any host and choose Security Profile.81.Choose SSH and click Options.82.Select Start and Stop with host and press Start.83.Connect to the host using an SSH client (e.g., Putty).84.Check the device list using the following command:esxcli storage nmp device list85.For all devices, reduce Round Robin size from 1000 to 1 using the following command:esxcli storage nmp psp roundrobin deviceconfig set --type=iops --iops=1 --device= NOTE:Paste StarWind device UID at the end of the cmdlet.86.Repeat the same steps for each host and datastore.87.Click the configuration tab on any host and choose Advanced Settings.88.Select Disk and change the Disk.DiskMaxIOSize parameter to 512.Creating a Datacenter89.Connect to vCenter, click Getting Started then Create Datacenter, and enter theDatacenter name.Creating a Cluster90.Navigate to the Datacenter Getting Started tab and press Create a cluster.91.Enter the cluster name and click Next.Adding Hosts to the Cluster92.Open the Cluster tab and click Add a host.93.Enter the name or IP address of the ESXi host and specify the administrative account.Click Next.。
DMM_虚拟机的动态内存映射模型
引用格式:陈昊罡,汪小林,王振林,等.DMM:虚拟机的动态内存映射模型.中国科学:信息科学,2010,40:1543–1558论文中国科学:信息科学2010年第40卷第12期:1543∼1558 DMM:虚拟机的动态内存映射模型陈昊罡x ,汪小林x *,王振林y ,张彬彬x ,罗英伟x *,李晓明xx 北京大学计算机科学技术系,北京100871y Department of Computer Science,Michigan Technological University,Houghton MI 49931,USA*通信作者.E-mail:wxl@,lyw@收稿日期:2009–02–01;接受日期:2010–01–15国家重点基础研究发展计划(批准号:2007CB310900)、国家自然科学基金(批准号:90718028,60873052)、国家高技术研究发展计划(批准号:2008AA01Z112)和教育部-英特尔信息技术专项科研基金(批准号:MOE-INTEL-08-09,MOE-INTEL-10-06)资助项目摘要内存虚拟化方法一直是虚拟机管理器设计中最重要的部分.文中提出了VMM 进行内存管理的一种机制:虚拟机(VM)的动态内存映射模型,它允许VMM 在虚拟机运行时,动态地改变它的物理内存与机器物理内存的映射关系.利用DMM,VMM 向上能够方便地实现按需取页、页面交换、Ballooning 、内存共享、copy-on-write 等虚拟机高级内存管理技术,向下能够兼容多种虚拟化架构.它所提供的一种模块化的分层体系结构,能有效地将上层的内存管理策略和底层的内存虚拟化实现很好地融合起来,为实现特征可调的内存管理提供了可能.文中给出了动态内存映射模型的基本原理,并阐述了利用该模型,实现各种虚拟机内存管理技术的相应机制和步骤.同时,在一个开源的虚拟机管理器(KVM)上实现了动态内存映射机制.测试表明,该机制具有良好的灵活性和可扩展性,能够在充分保证虚拟机访问内存的性能的前提下,实现虚拟机内存的动态管理和调配.关键词虚拟机管理器Xen 虚拟机内存虚拟化动态内存映射1相关工作及问题的提出内存是虚拟机最频繁访问的设备之一,内存虚拟化的效率将对虚拟机的性能产生重大影响.现代计算机通常都采用段页式存储管理、多级页表等复杂的存储体系结构,这又给虚拟机管理器(virtual machine monitor,VMM)的高性能内存虚拟化设计带来了很大挑战.当需要在同一物理主机上同时部署多个虚拟机时,VMM 能否提供可伸缩的内存管理功能就显得尤为重要.这是因为VMM 需要实现物理内存在虚拟机之间的分割复用,如果这种分割是静态的,则一台物理主机上所能并发执行的虚拟机数量必然受到实际硬件的机器内存大小的限制.同时,由于虚拟机上运行的软件对内存的需求各不相同,而且是动态变化的,基于静态分割的内存管理机制必然会造成内存资源的不合理分配,从而大大影响虚拟机执行的性能.为了使VMM 系统具有更好的伸缩性和可扩展性,我们希望VMM 能够提供一种机制,使得能够在充分保证虚拟机访问内存的性能的前提下,实现虚拟机内存的动态管理和调配.例如,理想的VMM 应该提供以下一些内存管理功能[1]:陈昊罡等:DMM:虚拟机的动态内存映射模型(1)按需取页.只有当虚拟机真正需要的时候,VMM才将物理内存分配给它,而不是简单地将固定大小的内存空间划分给虚拟机.按需取页能够提高内存资源的利用率.(2)虚拟存储.VMM应该能够利用交换等技术,给虚拟机提供超过实际机器内存大小的内存空间.虚拟机上的Guest OS能够象运行在裸机上一样,透明地使用VMM提供的整个“物理内存”.(3)内存共享.VMM应该允许虚拟机之间只读地共享完全相同的内存区域,从而缓解大量虚拟机并发运行时的内存资源紧缺.内存共享是内存copy-on-write机制的重要基础.当前的VMM分别实现了以上部分管理功能.如开源虚拟机管理器Xen实现了基于Ballooning的虚拟存储技术[2∼4],即允许VMM从其他虚拟机窃取一些未使用机器内存页面,给急需内存的虚拟机使用;VMWare公司的VMWare Workstation实现了基于交换的虚拟存储技术[5],即允许将虚拟机的部分物理内存页面交换到宿主操作系统(host OS)的交换磁盘分区上;而VMWare ESX Server还实现了基于页面内容比较的虚拟机间内存共享技术[1].但是,由于各种内存管理功能的底层的实现机制是相互独立的,现有VMM中的这些内存管理机制具有以下缺点:第一,机制的可扩展性不强.受到开发周期的影响,上述VMM在实现不同的内存管理功能时,分别引入了不同的底层支持机制,导致内存管理模块日益复杂而难以管理,限制了系统的可扩展性.例如,为了实现Ballooning功能,Xen引入了Grant Table机制[6],但是该机制并不适用于按需取页、内存交换和内存共享,因而难以继续加入这些内存管理功能.第二,完整性和耦合度不高.现有VMM大都未能实现上述所有的内存管理功能.而即使是已实现的功能,也都分别采用独立的模块设计.由于模块所用到的底层机制是相互独立的,这些模块之间难以有效地协同工作,甚至是相互冲突的.例如在VMWare ESX Server中,内存共享机制是不能与交换机制同时启用的[1].而在实际应用中,我们往往希望VMM能够综合地、并发地利用所有可能的内存管理技术,实现资源利用率的最大化.第三,兼容性和可维护性差.现有VMM中许多内存管理功能的实现都依赖于某些特定的系统体系结构,如半虚拟化、影子页表或硬件辅助虚拟化.这种平台相关性使得我们在增改某种内存管理功能、或将现有功能移植到新的硬件及操作系统平台上时,常常牵一发而动全身.不但耗时费力,还容易留下错误及安全隐患.为了解决现有VMM系统中的内存管理机制相互独立所带来的代码可维护性差、可扩展性差、耦合度低等难题,需要有一套通用的、高效的、可扩展的虚拟机内存管理机制.为此,本文提出了虚拟机的动态内存映射(dynamic memory mapping,DMM)模型.DMM是VMM进行内存管理的一种底层机制,它允许VMM在虚拟机运行时,动态地改变虚拟机的物理内存与真实硬件的机器内存的对应关系,从而能够向上很好地支持VMM实现按需取页、虚拟存储和内存共享等内存管理功能,并向下兼容多种内存虚拟化的系统结构.图1显示了DMM所提供的模块化的分层体系结构.动态内存映射模型与底层的硬件和实现无关,但为实现上层的管理功能提供了必要的、统一的机制.DMM为实现特征可调的内存管理提供了可能:一方面,上层的内存管理策略可调;另一方面,底层的实现机制也可调.也就是说,DMM将上层的内存管理策略和底层的内存虚拟化实现很好地融合起来了.在接下来的章节中,第2节首先回顾虚拟机技术的理论基础,以及当前进行内存虚拟化的基本思路和主要实现方法,然后提出了DMM的理论模型,并从理论上阐述了该模型对于按需取页、虚拟存储、内存共享等虚拟机高级内存管理技术的适用性,形式化地给出了相应的方法步骤.第3节介绍了DMM在KVM上的实现,设计并实现了对该模型至关重要的页面池机制、违例陷入机制等;并通过测1544中国科学:信息科学第40卷第12期图1DMM提供的模块化分层体系结构图2机器地址、物理地址和虚拟地址试和分析得到了该机制的主要时间开销和空间开销.第4节是总结.2动态内存映射的理论模型VMM通常采用分割复用的思想来虚拟计算机的物理内存.也就是说,VMM需要将机器的内存分配给各个虚拟机,并维护机器内存和虚拟机所见到的“物理内存”的映射关系(f-map),使得这些内存在虚拟机看来是一段从地址0开始的、连续的物理地址空间.本节我们将首先阐释一些基本概念,然后讨论内存虚拟化中f-map的具体内容,并给出动态内存映射模型的形式化定义,最后我们将阐释动态内存映射模型对于第1节所提出的各种虚拟机内存管理功能的适用性.2.1研究基础现代计算机通常都具备内存分页保护机制,这给VMM进行内存虚拟化提供了必要硬件支持,因为VMM能够以页面为单位建立f-map,并利用页面权限设置实现不同虚拟机间内存的隔离和保护.但是,由于Guest OS本身也会进行页式内存管理,虚拟机系统中实际上存在着3个地址概念:(1)机器地址(machine address),指真实硬件的物理地址,即地址总线上应该出现的地址信号;(2)物理地址(guest physical address),指经过VMM抽象的、虚拟机所看到的伪物理地址;(3)虚拟地址(guest virtual address),指Guest OS提供给其应用程序使用的线性地址空间.显然,VMM通过f-map实现了“机器地址”到“物理地址”的映射,我们将这个映射记为f;同时,Guest OS的内存管理模块要完成“虚拟地址”到“物理地址”的映射,我们将这个映射记为g,则机器地址、物理地址和虚拟地址的关系如图2所示.可见在虚拟机环境下,虚拟地址必须经过两次映射方能得到总线上使用的机器地址.为了实现“虚拟地址”到“机器地址”的高效转换,现在普遍采用的思想是:由VMM根据映射f和g生成复合的映射f·g,并直接把这个映射关系交给处理器中的内存管理单元(memory management unit,MMU).这种思想的可行性在于:(1)VMM维护着映射f;(2)VMM能够访问Guest OS的内存,因此能够模拟1545陈昊罡等:DMM:虚拟机的动态内存映射模型MMU来查询Guest OS的页表,从而能够获得映射g;(3)计算复合映射f·g能够在恰当的时候高效地进行.尽管基本思路是一样的,但现有的VMM针对这个思路分别采取了不同的实现方法.2.1.1MMU半虚拟化(MMU para-virtualization)半虚拟化指的是经过VMM抽象的虚拟机体系结构与实际硬件略有不同,并以此为代价降低VMM本身的复杂性,同时获得更好的性能[7].MMU的半虚拟化方法实现简单,VMM将映射关系f·g直接写入Guest OS的页表中,并替换原来的映射g.为了保证替换后Guest OS仍然能够正常工作,半虚拟化要求修改Guest OS的少量源代码.剑桥大学的Xen系统,是采用半虚拟化技术的VMM的典型代表[2∼4].2.1.2影子页表(shadow page table)与半虚拟化不同,影子页表技术为Guest OS的每个页表维护一个“影子页面”,并将合成后的映射关系f·g写入到“影子”中,而保持Guest OS的页表内容不变.最后,VMM将影子页表交给MMU 进行地址转换.由于影子页表的分配和维护完全是在Guest OS之外的VMM中进行的,因而Guest OS 是完全透明的,能够适用于无法获得源代码的操作系统(如Microsoft Windows)的虚拟化.VMWare Workstation、VMWare ESX Server以及KVM都使用了影子页表技术[1,5,8].2.1.3硬件辅助虚拟化(EPT或NPT)近年来,Intel和AMD分别提出了扩展页表(extended page tables,EPT)[9]和嵌入页表(nested page tables,NPT)技术[10],它们允许VMM直接将映射关系f和g分别交由MMU,并由硬件自动完成两级的地址转换,从而大大简化了VMM的设计.但由于双重页表机制使得遍历页表结构需要更多的访存操作,进而会影响总体性能.为了降低地址转换开销,处理器通常也要将部分合成后的映射关系f·g缓存在快表(translation look-aside buffer,TLB)中.MMU半虚拟化、影子页表和硬件辅助虚拟化的比较如图3所示.由于本节中讨论的关于动态内存映射的理论模型与具体实现无关,因此能够适用于上述3种内存虚拟化的方法.2.2虚拟机的简单内存映射为虚拟机内存构造f-map最简单的方法就是建立物理地址到机器地址的一一映射关系.形式化地说,我们定义如下概念:集合P:虚拟机所见到的物理内存;集合Z:真实计算机上机器内存总和;集合M:VMM提供给该虚拟机的机器内存.M是Z的一个真子集.单射f:从P到M的一个全函数,即对于任意的p∈P,都存在唯一的m∈M与之对应,且f 在虚拟机的生存周期内是不变的.虚拟机的简单内存映射如图4所示.在这里,单射f就是VMM为进行内存虚拟化所维护的f-map.实现这种内存管理模式是非常简单的:对于每个虚拟机,我们可以用一个数组(通常以页面为单位)来保存映射f,这个数组在虚拟机创建时被初始化并赋值,使得数组的每一元素都指向不同的机器页面.需要查询f-map时,直接用Guest OS的物理页面号(guest frame number,GFN)作为下标读取数组元素,便可获得对应的机器页面号(machine frame number,MFN),其时间开销为常数.当然,这种数据结构和算法的可行性是建立在单射、全函数、生存周期内不变等假设之上的.1546中国科学:信息科学第40卷第12期图3半虚拟化、影子页表以及硬件辅助虚拟化的比较图4虚拟机的简单内存映射但是,简单内存映射模式无法实现第1节所提到的按需取页、虚拟存储、内存共享等高级内存管理功能.这是因为:第一,这些功能要求集合M和映射f在虚拟机执行过程中是可变的;第二,这些功能要求某些物理内存可以没有机器内存与之对应,即f不一定是全函数;第三,内存共享要求f不一定是单射.尽管存在上述局限性,由于这种模式的简单和高效性,它仍然被一些VMM所采用,例如KVM[8].2.3动态内存映射的定义我们仍然记虚拟机所见到的物理内存为集合P,虚拟机的生存周期为序列t0,t1,...,t h,假设在时刻t i时,VMM提供给该虚拟机的机器内存为集合M i,映射f i是从P到M i的一个部分函数,即对于任意的p∈P,f i(p)=∅或f i(p)={m},m∈M i,且满足下列性质:(1)存在某个i∈{0,1,...,h−1},使得f i=f i+1,或者M i=M i+1;(2)若f i(p)=∅,则虚拟机对内存p的读写访问都将陷入VMM;(3)f i可能不是单射,且对任意两个不同的p1,p2∈P,若f i(p1)=f i(p2),则虚拟机对内存p1或者p2的写访问都将陷入VMM;我们就称函数序列F=f0,f1,...,f h是虚拟机内存P的一个动态内存映射.在动态内存映射的定义中,性质(1)保证了作为f-map的映射f i和其值域M i都是能够随着时间变化的,因此我们允许VMM动态调整虚拟机使用的机器内存集合,或者动态调整虚拟机的物理内存到机器内存的对应关系;性质(2)允许虚拟机的某些物理内存没有机器内存与之对应,而且保证VMM 能够介入虚拟机对这些内存的访问(包括读和写),并进行模拟;性质(3)允许虚拟机的某些物理内存共享同一机器内存,而且保证VMM能够介入虚拟机对这些内存的写访问,并进行模拟.图5显示了某个时刻时动态内存映射的工作情况.要注意到在上述定义中,虚拟机所见到的物理内存集合P在虚拟机的生存周期中是不可变化的.其原因是当前多数的硬件及操作系统都不支持内存的热插拔,因此1547陈昊罡等:DMM:虚拟机的动态内存映射模型图5虚拟机动态内存映射图6虚拟机的生存周期序列Guest OS所见到的物理内存空间大小通常是恒定的,我们的VMM也要提供这种硬件抽象,以保证虚拟环境的兼容性.在动态内存映射的定义中,需要进一步明确的是生存周期序列的概念.我们将虚拟机的生存周期抽象为一系列离散的时间点是有根据的.如图6所示,VMM在t0时刻启动虚拟机,并在t h时刻销毁虚拟机,这两个时间点之间的连续时间区间称为虚拟机的生存周期T.而虚拟机的执行实际上是CPU控制权在VMM和Guest OS(包括其上的应用程序)之间交替的过程.不失一般性地,这里只考虑VMM上运行1台虚拟机的情况.一方面,当CPU控制权在VMM中时,如时间段[t ,t i),VMM才能够修改和维护f-map,但是由于Guest OS不在运行中,这期间f-map的变化对Guest OS是没有影响的;另一方面,当CPU控制权在Guest OS中时,如时间段(t i−1,t ],由于f-map对Guest OS是不可见的,可以保证这期间f-map一定不会发生变化.因此,f-map的变化对虚拟机的执行能够产生影响的关键时刻就是CPU 控制权即将切换到Guest OS之时,而不用考虑(t i−1,t i)之间的时间.所以,我们用离散的时间点序列t0,t1,...,t h来考察映射f及集合M的变化是完全合理的.2.4动态内存映射的适用性本节将根据2.3小节给出的动态内存映射的定义,从理论上阐释这种模型对于按需取页、虚拟存储、内存共享等内存管理技术的适用性.而且,我们将给出利用动态内存映射模型实现这些技术的具体步骤.2.4.1按需取页按需取页(demand paging)指的是只有当虚拟机真正需要的时候,VMM才将机器内存分配给它, 1548中国科学:信息科学第40卷第12期并动态地建立该物理内存与机器内存的映射关系.在动态内存映射模型下,按需取页可以用如下步骤实现:(1)VMM初始化M0=∅,且对于任意的p∈P,设置f0(p)=∅,并启动虚拟机;(2)在时刻t i,若虚拟机访问了内存p,且f i(p)=∅,则根据性质(2),虚拟机的执行将中断,控制转入VMM;(3)VMM分配一块新的机器内存m,并设置M i+1=M i∪{m},设置f i+1(p)={m},然后恢复虚拟机执行;(4)重复步骤(2)和(3),直到虚拟机停机.2.4.2虚拟存储虚拟机系统中的虚拟存储(virtual memory)技术与操作系统中类似,即允许虚拟机使用超过实际机器内存大小的内存空间[11].VMM实现虚拟存储的方法主要有两种:页面交换和Ballooning,下面我们将分别讨论.2.4.2.1页面交换页面交换技术(swapping)是现代操作系统中广泛使用的虚拟内存技术[11],它允许操作系统将某些非活动的内存页面保存到内存以外的“交换空间”上(通常是磁盘).虚拟机的页面交换技术和操作系统的页面交换技术是十分类似的[12],它必须建立在按需取页机制的基础之上.在动态内存映射模型下,页面交换可以用如下步骤实现:(1)VMM初始化M0=∅,且对于任意的p∈P,设置f0(p)=∅,并启动虚拟机;(2)在时刻t i,若虚拟机访问了内存p,且f i(p)=∅,则根据性质(2),虚拟机的执行将中断,控制转入VMM;(3)VMM判断当前机器内存是否充裕,若是,则VMM分配一块新的机器内存m,并设置M i+1= M i∪{m},设置f i+1(p)={m},然后转到步骤(5);(4)否则,VMM需要将某些页面换出.VMM根据某种策略(如LRU)从页面集合M i中选择一个非空子集S,对于任意的页面s∈S,设置M i+1=M i−{s},同时,对于所有的x∈P且f i(x)=s,我们设置f i+1(x)=∅,并将s的内容写入交换空间.然后回到步骤(3);(5)VMM检查p的内容是否在交换空间中,若是,则从交换空间中将p的内容读入m;(6)恢复虚拟机的执行,转到步骤(2),直到虚拟机停机.2.4.2.2BallooningBallooning(气球技术)是虚拟机系统中所特有的虚拟存储技术[1].它的基本思想是:从其他虚拟机窃取一些未使用机器内存页面,给急需内存的虚拟机使用.为了实现内存的窃取,VMM需要在Guest OS的内核中安装一个用于窃取内存的模块,称作“balloon driver”.Ballooning通常包括两个过程:VMM通过“气球膨胀(balloon inflating)”过程从虚拟机回收空闲的内存资源,而通过“气球收缩(balloon deflating)”过程将先前回收的内存返回给虚拟机.在动态内存映射模型下,Ballooning可以用如下步骤实现,为了简单起见,我们只考虑两台虚拟机的情况.1.气球膨胀(balloon inflating)(1)VMM为两台虚拟机VM和VM 分别初始化M0和M 0,f0和f 0,并启动这两台虚拟机.我们假设M0和M非空,且存在p∈P,f0(p)=∅;1549陈昊罡等:DMM:虚拟机的动态内存映射模型(2)在时刻t i,若虚拟机VM访问了内存p,且f i(p)=∅,则根据性质(2),虚拟机的执行将中断,控制转入VMM;(3)VMM判断M i中是否还有未被f i映射的元素m,即m∈M i−f i(P).若是,则VMM设置f i+1(p)={m},设置M i+1=M i,然后转到步骤(6);(4)否则,VMM将向另一台虚拟机VM 的balloon driver发出“气球膨胀”请求,balloon driver将从它的页面集合Mi 中获得一个非空子集S ,对于任意的页面s ∈S ,设置Mi+1=M i−{s },同时,对于所有的x ∈P 且fi(x )=s ,我们设置f i+1(x )=∅;(5)VMM在虚拟机VM上设置M i=M i∪S .然后回到步骤(3);(6)恢复虚拟机的执行,转到步骤(2),直到虚拟机停机.2.气球收缩(balloon deflating)(1)由于“气球膨胀”过程的步骤(4),存在时刻t i以后的某个时刻t j,存在p ∈P ,且f j(p )=∅;(2)在时刻t j(j>i),若虚拟机VM 访问了内存p ,且f j(p )=∅,则根据性质(2),虚拟机的执行将中断,控制转入VMM;(3)VMM判断M j中是否还有未被f j映射的元素m ,即m ∈M j−f j(P ).若是,则VMM设置f j+1(p )={m },设置M j+1=M j,然后转到步骤(6);(4)否则,VMM将向VM 的balloon driver发出“气球收缩”请求,由于balloon driver仍然保存着“气球膨胀”过程中获得的非空子集S ,对于任意的页面s ∈S ,VMM将为虚拟机VM分配一个空闲的机器页面m,将s 的内容复制到m中,并设置M j+1=M j∪{m}−{s },同时,对于所有的x∈P且f j(x)=s ,我们设置f j+1(x)=m;(5)VMM在虚拟机VM 上设置M j=M j∪S .然后回到步骤(3);(6)恢复虚拟机的执行,转到步骤(2),直到虚拟机停机.上述步骤(4)中,若VMM无法为虚拟机VM分配一个空闲的机器页面m,则VMM可能发起另一个页面交换或气球膨胀过程,在此不再赘述.2.4.3内存共享和copy-on-write内存共享指的是VMM允许虚拟机之间只读地共享完全相同的内存区域.内存共享能够缓解大量虚拟机并发运行时的内存资源紧缺.例如,同一台物理主机上的多个虚拟机可能运行着同一类Guest OS的多个实例,或者运行着相同的应用程序、组件代码,或者载入了相同的数据集.在这种情况下,允许虚拟机之间共享相同的内存区域将大大提高内存资源的利用率[13].为了保证虚拟机之间的隔离性,VMM通常不允许不同的虚拟机对同一页面进行可写共享.因此,如果某台虚拟机对一个共享的页面执行了写操作,我们就必须将共享的页面复制一份,并将修改反映在副本上,以保证该页面的内容对其他虚拟机是不变的.这个过程就是copy-on-write(COW).COW是类Unix操作系统创建新进程时采用的主要技术,我们认为,在虚拟机系统中,它同样可以被用于虚拟执行环境的快速复制[14].copy-on-wirte的工作原理如图7所示.内存共享和copy-on-write机制也可以用动态内存映射模型实现.由于多虚拟机共享内存和单虚拟机共享内存的实现步骤从本质上是一样的,为了叙述方便,我们将动态内存映射的定义扩展如下:记VMM上运行的所有虚拟机为集合V,其中某个虚拟机v∈V所见到的物理内存为集合P v.定义P={ v,p |v∈V∧p∈P v},它表示所有虚拟机的所有物理内存.假设在时刻t i时,VMM提供给所有虚拟机的机器内存为集合M i.映射f i是从P到M i的一个部分函数,且满足2.3小节给出的3个性质.1550中国科学:信息科学第40卷第12期图7内存copy-on-write的工作原理图8应用动态内存映射模型的基本流程在上述定义下,内存共享和copy-on-write的步骤可以描述如下:(1)VMM为各个虚拟机初始化M0={s},并对于任意的 v,p ∈P,设置f0( v,p )={s},然后依次启动各个虚拟机;(2)在时刻t i,虚拟机v试图写入内存 v,p .若存在 v ,p ∈P, v ,p = v,p ,但f i( v,p )=f i( v ,p ),则根据性质(3),虚拟机的执行将中断,控制转入VMM;(3)VMM分配一个新的机器页面m,将页面f i( v,p )的内容复制到m中.然后,VMM设置M i+1=M i∪{m},设置f i+1( v,p )={m},并恢复虚拟机的执行;(4)重复步骤(2)和(3),直到所有虚拟机停机.在上述步骤中,所有虚拟机的内存一开始都映射到同一个机器页面,并随着它们运行的过程利用copy-on-write不断分裂.这从理论上是可行的,但是考虑到性能问题,实际的VMM通常不会采取如此激进的策略.从以上内容我们可以看到,动态内存映射模型能够很好地支持按需取页、虚拟存储、内存共享等内存管理功能,因此该模型具有很强的适用性.尽管实现这些功能的方法各不相同,但其思想都是一样的:利用f-map的违例陷入机制,由VMM介入并模拟违例的行为,或者对资源进行修复和再分配.例如,按需取页、虚拟存储机制是利用动态内存映射模型的性质(2)来实现违例陷入,而内存共享和COW则是利用该模型的性质(3)来实现违例陷入.图8显示了实现这些功能的基本流程.3动态内存映射机制的实现及评估本节将探讨在开源的VMM——KVM(kernel-based virtual machine)[8]上具体实现该机制的必要步骤和主要技术难点.我们的实现主要用于验证动态内存映射模型是可行实用的,同时给出实现过程中一些关键技术问题的解决方法.从程序设计的角度看,实现动态内存映射机制将涉及以下问题:(1)如何维护用于虚拟化的物理资源集合M,并且使得M是可变的？(2)如何表示和维护从P到M的映射关系f,并且使得f是可变的？(3)如何保证当发生f-map违例时,VMM能够获得计算机的控制权？为了解决问题(1),我们提出了“页面池”的概念,它是虚拟机物理资源集合M的一个具体实现;为了解决问题(2),我们设计了用于完成映射的主要数据结构,并描述了该数据结构是如何与页面池协同工作的;为了解决问题(3),我们详细地考察了KVM中影子页表和页故障的处理机制,并用逆映射(reverse mapping)的方法实现了一个安全的页面回收机制,以保证在发生f-map违例时,Guest OS会由于页故障而陷入VMM.1551。
OTC 深海技术会议2009年会议论文全部标题——中英文对照
34.
19858
Tahiti Flowline Expansion Control System
Tahiti油田出油管线的膨胀控制系统
35.
19859
Tahiti Project Subsea System Design/Qualification
Tahiti项目水下系统的设计/认证
19783
Novel Single-Trip Upper Completion System Saves Rig Time in Deepwater Offshore Brazil
巴西深水节省钻机时间的新型Single-Trip完井技术
8.
19784
Development of a Large Bore Umbilical for Deep Water Service
世界上第一个针对水下采掘的大型浮式选矿机的介绍
23.
19826
Risk Mitigation of Chemical Munitions in a Deepwater GeoHazard Assessment
如何减轻深水地质灾害评估中化学物的风险
24.
19835
Comparison of Tank Testing and Numerical Analysis for the Design of a Catamaran for Deck Installation by the Float-Over Method
超深水中水下安全阀控制系统的应用
44.
19871
Effect of Remolding and Reconsolidation on the Touchdown Stiffness of a
基于虚拟机迁移的虚拟机集群资源调度_周文煜陈华平杨寿保方君
目前, 在服务器虚拟化技术的应用领域, 多采 用静态的资源分配方法, 虚拟机对负载变化不具 有良好的适应性. 在虚拟机集群的调度技术方面, 大部分应用仍然使用传统物理机集群下使用的基 于任务调度的策略[ 5] . 由于任务型的应用不能覆 盖所有类型的应用; 因此, 基于任务调度的集群调 度方法具有一定的局限性, 并且调度粒度较大, 难 以发挥出虚拟机集群本身具有的特殊优势.
虚拟机的资源分配可以从单结点内和节点间
2 方面进行. 在半虚拟化模式下, 单节点内可以通 过改变不同虚拟机 的 CP U 与内存分配参 数, 从 而使节点内各个虚拟机占用物理机资源的比例改 变. 与 虚 拟 机 CP U 性 能 相 关 的 参 数 有: a. VCPU , 即虚拟 CPU 的数量; b. Weight , 即虚 拟机 CPU 占用 VMM 所 分配物理 CPU 时间片 的权重; c. Cap, 即虚拟机 CPU 占用物理 CPU 的 最大比例值. 影响虚拟机内存资源分配的参数为 Memo ry Size, 即虚拟机分配所得内存大小. 在性 能监控数据和有效的调度算法配合下, 可以使节 点内 的虚拟 机的 CP U 和内 存资源 达到负 载平 衡. I/ O 和网络没有相关的参数可以在单节点内 改变资源分配. 2. 3 多节点的资源调度
应用对资源的需求特性, 应用根据对不同资源的 需求特点可分为 CPU 密集型、内存密集型、I/ O 密集型与网络密集型, 相应地将虚拟机资源也分 为 CPU, Memo ry, Net w ork 和 I/ O. 4 类资源的调 度方式各有其特点.
CP U 和 Memo ry 在半虚拟化模式下可以进 行单结点资源调度, 即单结点内的动态资源调整, 提高或降低资源分配; 在共享 I/ O 存 储方式下, 可以进行全局资源调度, 即把虚拟机迁移到不同 的节点, 从而改变资源的访问性能, 还可进一步在 源节点和目标节点上分别进行单节点调度.
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
A Technical Review for Efficient Virtual Machine Migration
Sangeeta Sharma , Meenu Chawla Department Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal, 462051, India sanjsharma29@gmail.com, chawlam@manit.ac.in
Abstract—This paper presents the recent technical research survey on the efficient live migration of virtual machines. Virtual machine migration is required for many reasons like load balancing, energy reduction, dynamic resizing, and to increase availability. Live migration is an important feature of virtualization technology. Virtualization technology has gained toehold with ascend of cloud computing. Simply, virtualization runs multiple virtual machines on the single physical machine.
Virtual machine is a software based component which is an abstraction of the underlying hardware provided by the virtualization technology. Availability can be efficiently achieved by managing the virtual machines properly. For this, there is a need to migrate virtual machine from one physical machine to another, without expending long migration time and downtime. Virtual machine is running, while the migration is in progress is known as live migration. There are many techniques proposed to enhance the live migration, and reduce its duration
This paper reviews the different approaches which have been proposed by researchers to improve the live migration by decreasing the downtime and total migration time. This paper can be useful for the researchers to work on this field.
Keywords— live migration; cloud computing; virtualization; xen; downtime.
I.INTRODUCTION
Cloud Computing provides the sharing of multiple resources between several users at the same time by the use of internet. It is also highly scalable, elastic in nature. It works on pay as you use model. Virtualization technology can play an important role to provide the multi-tenancy effectively. Virtualization gives an abstract view of hardware by means of virtual instance of multiple guest operating systems on a single host operatingsystem. This virtual instance of operating system is known as virtual machine. The user can access the hardware in terms of virtual machines, which can simulate the hardware. As many virtual machines can be created on a single physical machine, depending on their capacity. Itis clear that by proper management of virtual machines can improve the resource utilization and availability. Virtualization allows a migration of virtual machine from one physical machine to another. Migration can be offline where user is halted till the virtual machine can resume on target machine. Another is live migration in which user is able to continuously execute during the migration. Migration of entire operating system and all of its applications as one unit (i.e. virtual machine) is less complex than process level migration. To avoid the dependency between the physical host machine and virtual machine, virtual machine monitor (hypervisor) is used as an interface. It is simply a software layer between host operating system and virtual operating system. One of the widely used virtual machine monitor is Xen [1]. It is a high performance resource managed virtual machine monitor, which allows various virtual machines to share common hardware in a safe and resource managed fashion, but without sacrificing the performance and functionality. Migration of entire virtual machines means that in-memory state can be transferred in a consistent and efficient fashion. This includes internal states of kernel level and application level. Live virtual machine migration is an extremely powerful tool for the cloud managers. Two important parameters to describe the performance of the migration are: xTotal migration time which shows the total time needed for migration of virtual machine and to start it on target machine. xDowntime is duration of time for which a user waits to resume the virtual machine. It is required to minimize both downtime and total migration time for the high performance of live migration. In the migration process, complete state of running virtual machine has to be transferred. The state of virtual machine includes the permanent storage (i.e. the disks), volatile storage (the memory), the state of connected devices (such as network interface card), and the internal state of the virtual CPU. Generally the permanent memory is provided by means of network attached storage (NAS); so it is no required to move it. The internal state of the VCPU and connected devices are a few kilobytes of data and can be easily sent to the target host. So, the main focus of live migration is efficient transfer of volatile memory which may be up to several gigabytes.This paper presents the different techniques to improve the performance of live migration by transferring the memory efficiently. It can be optimized by reducing the total migration time and downtime but encompasses considerable overhead. Memory transfer can be categorized into three phases [2]: A.Memory Transfer Phases: