LEADER 05142nam 22006251 450 001 9910790634903321 005 20200520144314.0 010 $a1-78217-769-8 035 $a(CKB)2550000001136104 035 $a(EBL)1507841 035 $a(OCoLC)862050193 035 $a(SSID)ssj0001139826 035 $a(PQKBManifestationID)11659959 035 $a(PQKBTitleCode)TC0001139826 035 $a(PQKBWorkID)11220138 035 $a(PQKB)11558527 035 $a(MiAaPQ)EBC1507841 035 $a(Au-PeEL)EBL1507841 035 $a(CaPaEBR)ebr10788170 035 $a(CaONFJC)MIL535807 035 $a(PPN)228046211 035 $a(EXLCZ)992550000001136104 100 $a20131207h20132013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMicrosoft Hyper-V cluster design /$fEric Siron 210 1$aBirmingham :$cPackt Publishing,$d[2013] 210 4$dİ2013 215 $a1 online resource (462 p.) 300 $aIncludes index. 311 $a1-78217-768-X 311 $a1-306-04556-8 327 $aCover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Hyper-V Cluster Orientation; Terminology; Clustering in a Microsoft environment; Create a project document; Purposes for a Hyper-V Server cluster; High availability; High Availability Printing; Balancing resources; Geographic dispersion; Natural replacement for aging infrastructure; Test, development, and training systems; Cloud hosting; Resource metering; VDI and RemoteFX; Be open to other purposes; Goals for a Hyper-V Server cluster 327 $aIdentify the resources that cannot be virtualizedConsult with application vendors; Involve internal stakeholders; Define phases and timelines; Perform further research; Define success metrics; Measure and predict your workload; Only allow changes during the planning phase; Looking forward to the Design phase; Host computers; Storage; Cluster Shared Volumes; SMB shares; Mixing SMB 3.0 and CSV; Networking; Management; Cluster and Cluster Shared Volumes; Live Migration; Subnetting; Virtual machine traffic; Storage traffic; Physical adapter considerations; Adapter teaming; Active Directory 327 $aVirtualized domain controllersSupporting software; Management tools; Backup; Training; A sample Hyper-V Cluster planning document; Sample project title - Techstra Hyper-V Cluster Project; Sample project - purposes; Sample project - goals; Review the sample project; Summary; Chapter 2: Cluster Design and Planning; Starting the design phase; Planning for existing systems; Deciding how you will virtualize physical systems; Determining requirements for existing systems; Microsoft Assessment and Planning Toolkit; Performance Monitor; General approaches to reading the metrics; Memory measurements 327 $aNetwork measurementsDisk measurements; Processor measurements; Host computer components; Hyper-V Server requirements; CPU; Memory; Host networking; Host storage; Management operating system; Hyper-V Server; Windows Server; Deciding on a management operating system; Networking; Advanced networking hardware; Shared storage; Storage area network devices; Network-attached storage devices; General purpose computers; Shared storage performance characteristics; Designing shared storage; Software licensing; Windows Server and guest virtualization rights; Software Assurance; Client access licenses 327 $aOther software licensesHyper-V and cluster-related software planning; Remote software applications; Local software applications; Blade hardware; Physical placement; Security; Domain separation; Hyper-V isolation; Network isolation; Complete the planning phase; Sample project - planning and design; Sample project - hardware; Summary; Chapter 3: Constructing a Hyper-V Server Cluster; Documenting the initial setup phase; Build steps not covered in this book; Auxiliary built-in tools; Acquiring and enabling the GUI tools; Enabling the tools on Windows 8/8.1 from the GUI 327 $aEnabling the tools on Windows Server 2012/R2 in the GUI 330 $aThis book is written in a friendly and practical style with numerous tutorials centred on common as well as atypical Hyper-V cluster designs. This book also features a sample cluster design throughout to help you learn how to design a Hyper-V in a real-world scenario.Microsoft Hyper-V Cluster Design is perfect for the systems administrator who has a good understanding of Windows Server in an Active Directory domain and is ready to expand into a highly available virtualized environment. It only expects that you will be familiar with basic hypervisor terminology. 606 $aSystem design 606 $aSystem design$xData processing 615 0$aSystem design. 615 0$aSystem design$xData processing. 676 $a005.4476 700 $aSiron$b Eric$01487952 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910790634903321 996 $aMicrosoft Hyper-V cluster design$93708080 997 $aUNINA LEADER 04142nam 22006135 450 001 9911047689003321 005 20251118120409.0 010 $a9783032000521 024 7 $a10.1007/978-3-032-00052-1 035 $a(CKB)43368487200041 035 $a(MiAaPQ)EBC32420107 035 $a(Au-PeEL)EBL32420107 035 $a(DE-He213)978-3-032-00052-1 035 $a(EXLCZ)9943368487200041 100 $a20251118d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHidden Markov Processes and Adaptive Filtering /$fby Yury A. Kutoyants 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (1042 pages) 225 1 $aSpringer Series in Statistics,$x2197-568X 311 08$a9783032000514 327 $a1 Auxiliary Result -- 2 Small Noise in Both Equations -- 3 Small Noise in Observations -- 4 Hidden Ergodic O-U process -- 5 Hidden Telegraph Process -- 6 Hidden AR Process -- 7 Source Localization. 330 $aThis book is devoted to the problem of adaptive filtering for partially observed systems depending on unknown parameters. Adaptive filters are proposed for a wide variety of models: Gaussian and conditionally Gaussian linear models of diffusion processes; some nonlinear models; telegraph signals in white Gaussian noise (all in continuous time); and autoregressive processes observed in white noise (discrete time). The properties of the estimators and adaptive filters are described in the asymptotics of small noise or large samples. The parameter estimators and adaptive filters have a recursive structure which makes their numerical realization relatively simple. The question of the asymptotic efficiency of the adaptive filters is also discussed. Readers will learn how to construct Le Cam?s One-step MLE for all these models and how this estimator can be transformed into an asymptotically efficient estimator process which has a recursive structure. The last chapter covers several applications of the developed method to such problems as localization of fixed and moving sources on the plane by observations registered by K detectors, estimation of a signal in noise, identification of a security price process, change point problems for partially observed systems, and approximation of the solution of BSDEs. Adaptive filters are presented for the simplest one-dimensional observations and state equations, known initial values, non-correlated noises, etc. However, the proposed constructions can be extended to a wider class of models, and the One-step MLE-processes can be used in many other problems where the recursive evolution of estimators is an important property. The book will be useful for students of filtering theory, both undergraduates (discrete time models) and postgraduates (continuous time models). The method described, preliminary estimator + One-step MLE-process + adaptive filter, will also be of interest to engineers and researchers working with partially observed models. 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aMarkov processes 606 $aStochastic models 606 $aStatistics 606 $aProbabilities 606 $aMarkov Process 606 $aStochastic Modelling in Statistics 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aProbability Theory 615 0$aMarkov processes. 615 0$aStochastic models. 615 0$aStatistics. 615 0$aProbabilities. 615 14$aMarkov Process. 615 24$aStochastic Modelling in Statistics. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aProbability Theory. 676 $a519.233 700 $aKutoyants$b Yu. A$0442032 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911047689003321 996 $aHidden Markov Processes and Adaptive Filtering$94469174 997 $aUNINA