DistributedSystems(3rd)英文无水印原版pdf第3版pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开本资源转载自网络，如有侵权，请联系上传者或csdn删除查看此书详细信息请在美国亚马逊官网搜索此书Copyright@2017MaartenvanSteenandAndrewS.TanenbaumPublishedbyMaartenvanSteenThisbookwaspreviouslypublishedby:PearsonEducation,IncISBN:978-15-430573-8-6(printedversion)ISBN:978-90-815406-2-9(digitalversion)Edition:3.Version:01(February2017)AllrightstotextandillustrationsarereservedbyMaartenvanSteenandAndrewS.Tanenbaum.Thisworkmaynotbecopied,reproduced,ortranslatedinwholeorpartwithoutwrittenpermissionofthepublisher,exceptforbriefexcerptsinreviewsorscholanyformofinformationstorageadaptationorwhatever,computersoftware,orbysimilarordissimilarmethodsnowknownordevelopedinthefutureisstrictlyforbiddenwithoutwrittenpermissionofthepublisherToMarielle,max,andelkeMVSToSuzanneBarbara,Marvin,Aronnathan,olivia,andmirteASTCONTENTSPreface1Introduction1.1Whatisadistributedsystem?Characteristic1:Collectionofautonomouscomputingelements2Characteristic2:SinglecoherentsystemMiddlewareanddistributedsystems1.2DesigngoalsSupportingresourcesharingMakingdistributiontransparent12Beingscalable15Pitfalls243Typesofdistributedsystems24Highperformancedistributedcomputing25Distributedinformationsystems34Pervasivesystems1.4Summary522Architectures552.1Architecturalstyles56Layeredarchitectures.57Object-basedandservice-orientedarchitectures62Resource-basedarchitectures64Publish-subscribearchitectures2.2MiddlewareorganizationWrappersInterceptors垂番Modifiablemiddleware752.3SystemarchitectureCONTENTSCentralizedorganizations76Decentralizedorganizations:peer-to-peersystemsHybridarchitectures2.4Examplearchitectures94TheNetworkFilesystem94TheWeb982.5Summary3Processes1033.1Threads..104Introductiontothreads104Threadsindistributedsystems1113.2Virtualization116Principleofvirtualizationapplicationofvirtualmachinestodistributedsystems,1223.3Clients124Networkeduserinterfaces124Client-sidesoftwarefordistributiontransparency1273.4Servers128Generaldesignissues129Objectservers133Example:TheApacheWebserver139Serverclusters,,,,,,,1413.5Codemigration152Reasonsformigratingcode152Migrationinheterogeneoussystems1583.6Summary1614Communication4.1Foundations164LayeredProtocols164TypesofCommunication.1724.2Remoteprocedurecall..173Basicrpcoperation174Parameterpassing178RPC-basedapplicationsupport182VariationsonrPc185Example:dCErPc,.1884.3Message-orientedcommunication193Simpletransientmessagingwithsockets.193Advancedtransientmessaging198Message-orientedpersistentcommunication206Example:IBM'sWebSpheremessage-queuingsystem212Example:AdvancedMessageQueuingProtocol(AMQP)....218DS3.01DOWNLOADEDBYTEWIGOMIXMAIL.INFOCONTENTS4.4Multicastcommunication221Application-leveltree-basedmulticasting221Flooding-basedmulticasting225Gossip-baseddatadissemination2294.5Summary2345Naming2375.1Names,identifiersandaddresses2385.2Flatnaming.241Simplesolutions241Home-basedapproaches245Distributedhashtables246Hierarchicalapproaches2515.3Structurednaming256Namespaces.256Nameresolution259Theimplementationofanamespace264Example:TheDomainNameSystem271Example:TheNetworkFileSystem2785.4Attribute-basednaming283Directoryservices283Hierarchicalimplementations:LDAP285Decentralizedimplementations2885.5Summary2946Coordination2976.1Clocksynchronization.298Physicalclocks299Clocksynchronizationalgorithms3026.2Logicalclocks310Lamport'slogicalclocks310Vectorclocks3166.3Mutualexclusion321322acentralizedalgorithm.322adistributedalgorithm323atoken-ringalgorithm.325adecentralizedalgorithm3266.4Electionalgorithms329Thebullyalgorithm.,..330Aringalgorithm332Electionsinwirelessenvironments333Electionsinlarge-scalesystems.3356.5Locationsystems336DOWNLOADEDBYTEWIGOMIXMAIL.INFODS301VIllCONTENTSGPS:GlobalPositioningSystem337WhengPsisnotanoption339Logicalpositioningofnodes3396.6Distributedeventmatching..343Centralizedimplementations3436.7Gossip-basedcoordination349asgregation349Apeer-samplingservice350Gossip-basedoverlayconstruction3526.8Summary3537Consistencyandreplication3557.1Introduction356Reasonsforreplication356Replicationasscalingtechnique3577.2Data-centricconsistencymodels358Continuousconsistency359Consistentorderingofoperations364Eventualconsistency3737.3Client-centricconsistencymodels375MonotonicreadsMonotonicwrites.379Readyourwrite380Writesfollowreads3827.4ReplicamanagementFindingthebestserverlocation383Contentreplicationandplacement..385Contentdistribution..388Managingreplicatedobjects3937.5Consistencyprotocols.396Continuousconsistency..........396Primary-basedprotocols398Replicated-writeprotocolsCache-coherence403Implementingclient-centricconsistency,,...4077.6Example:CachingandreplicationintheWeb4097.7Summar4208Faulttoleran4238.1Introductiontofaulttolerance424Basicconcepts.424Failuodels427Failuremaskingbyredundancy8.2Processresilience432DS3.01DOWNLOADEDBYTEWIGOMIXMAIL.INFO

Stanfordlarge-scale3DIndoorSpacesDataset(S3DIS)

DavidJ.HandDepartmentofMathematics,ImperialCollegeLondon,London,UKDataminingisthediscoveryofinteresting,unexpectedorvaluablestructuresinlargedatasets.Assuch,ithastworatherdifferentaspects.Oneoftheseconcernslarge-scale,‘global’structures,andtheaimistomodeltheshapes,orfeaturesoftheshapes,ofdistributions.

Theavailabilityoflargedatasetshasallowedresearcherstouncovercomplexpropertiessuchaslarge-scalefluctuationsandheterogeneitiesinmanynetworks,leadingtothebreakdownofstandardtheoreticalframeworksandmodels.Untilrecentlythesesystemswereconsideredashaphazardsetsofpointsandconnections.Recentadvanceshavegeneratedavigorousresearcheffortinunderstandingtheeffectofcomplexconnectivitypatternsondynamicalphenomena.Thisbookpresentsacomprehensiveaccountoftheseeffects.Avastnumberofsystems,fromthebraintoecosystems,powergridsandtheInternet,canberepresentedaslargecomplexnetworks.Thisbookwillinterestgraduatestudentsandresearchersinmanydisciplines,fromphysicsandstatisticalmechanics,tomathematicalbiologyandinformationscience.Itsmodularapproachallowsreaderstoreadilyaccessthesectionsofmostinteresttothem,andcomplicatedmathsisavoidedsothetextcanbeeasilyfollowedbynon-expertsinthesubject.

IfyouwanttolearnhowtobuildefficientuserinterfaceswithReact,thisisyourbook.AuthorsAlexBanksandEvePorcelloshowyouhowtocreateUIswiththissmallJavaScriptlibrarythatcandeftlydisplaydatachangesonlarge-scale,data-drivenwebsiteswithoutpagereloads.Alongtheway,

Theacceleratingdeploymentoflarge-scaleweb,cloud,BigData,andvirtualizedcomputingsystemshasintroducedseriousnewchallengesinperformanceoptimization.Untilnow,however,littlereliable,practicalinformationhasbeenavailabletoITprofessionalswhoareresponsibleforrunningthesesystemsefficientlyandcost-effectively.SystemsPerformance:EnterpriseandtheCloudisthesolution.InternationallyrenownedperformanceoptimizationexpertBrendanGreggbringstogetherstate-of-the-arttechniquesandtoolsforanalysisandtuningoflarge-scaleweb/cloudcomputingenvironments.GreggfocusesonLinux/Unix/Solarisperformanceissues,whileofferingprovenmethodologiesanddiscussingkeyissuesthatapplytoallenterpriseoperatingsystems.Coverageincludes:Modernperformanceanalysisandcapacityplanning,includingkeyissuessuchaslatencyanddynamictracingNewperformanceandreliabilitychallengesassociatedwithcloudcomputingMethodology,concepts,terminology,tools,andmetricsKeytradeoffs,includingproblemsofloadvs.architectureTuningoperatingsystems,CPUs,memory,filesystems,disks,networks,andbussesTuningvirtualizedsystemsProgramminglanguageissuesrelatedtoperformance—includingapplicationprofilingforC,C++,Java,andnode.jsBenchmarkingstrategiesandpitfalls,includingcustommicrobenchmarking

KDD2018滴滴派单算法论文。
Wepresentanovelorderdispatchalgorithminlarge-scaleon-demandride-hailingplatforms.Whiletraditionalorderdispatchapproachesusuallyfocusonimmediatecustomersatisfaction,theproposedalgorithmisdesignedtoprovideamoreefficientwaytooptimizeresourceutilizationanduserexperienceinaglobalandmorefarsightedview.Inparticular,wemodelorderdispatchasalarge-scalesequentialdecision-makingproblem,wherethedecisionofassigninganordertoadriverisdeterminedbyacentralizedalgo-rithminacoordinatedway.Theproblemissolvedinalearningandplanningmanner:1)basedonhistoricaldata,wefirstsummarizedemandandsupplypatternsintoaspatiotemporalquantization,eachofwhichindicatestheexpectedvalueofadriverbeinginaparticularstate;2)aplanningstepisconductedinreal-time,whereeachdriver-order-pairisvaluedinconsiderationofbothimmedi-aterewardsandfuturegains,andthendispatchissolvedusingacombinatorialoptimizingalgorithm.ThroughextensiveofflineexperimentsandonlineABtests,theproposedapproachdeliversremarkableimprovementontheplatform’sefficiencyandhasbeensuccessfullydeployedintheproductionsystemofDidiChuxing.

清晰彩色Whenmostpeoplehear“MachineLearning,”theypicturearobot:adependablebutleroradeadlyTerminatordependingonwhoyouask.ButMachineLearningisnotjustafuturisticfantasy,it’salreadyhere.Infact,ithasbeenaroundfordecadesinsomespecializedapplications,suchasOpticalCharacterRecognition(OCR).ButthefirstMLapplicationthatreallybecamemainstream,improvingthelivesofhundredsofmillionsofpeople,tookovertheworldbackinthe1990s:itwasthespamfilter.Notexactlyaself-awareSkynet,butitdoestechnicallyqualifyasMachineLearning(ithasactuallylearnedsowellthatyouseldomneedtoflaganemailasspamanymore).ItwasfollowedbyhundredsofMLapplicationsthatnowquietlypowerhundredsofproductsandfeaturesthatyouuseregularly,frombetterrecommendationstovoicesearch.

大规模天线仿真，matlab，Large-ScaleMulti-User(MU)MIMO-OFDM系统仿真

KeyFeaturesLeverageCeph'sadvancedfeaturessuchaserasurecoding,tiering,andBluestoreSolvelarge-scaleproblemswithCephasatoolbyunderstandingitsstrengthsandweaknessestodevelopthebestsolutionsApracticalguidethatcoversengagingusecasestohelpyouuseadvancedfeaturesofCepheffectivelyBookDescriptionMasteringCephcoversallthatyouneedtoknowtouseCepheffectively.Startingwithdesigngoalsandplanningstepsthatshouldbeundertakentoensuresuccessfuldeployments,youwillbeguidedthroughtosettingupanddeployingtheCephcluster,withthehelpoforchestrationtools.KeyareasofCephincludingBluestore,Erasurecodingandcachetieringwillbecoveredwithhelpofexamples.DevelopmentofapplicationswhichuseLibradosandDistributedcomputationswithsharedobjectclassesarealsocovered.AsectionontuningwilltakeyouthroughtheprocessofoptimisizingbothCephanditssupportinginfrastructure.Finally,youwilllearntotroubleshootissuesandhandlevariousscenarioswhereCephislikelynottorecoveronitsown.Bytheendofthebook,youwillbeabletosuccessfullydeployandoperatearesilienthighperformanceCephcluster.WhatyouwilllearnKnowwhenandhowtousesomeofCeph'sadvancednewfeaturesSetupatestclusterwithAnsibleandsomevirtualmachinesusingVirtualBoxandVagrantDevelopnovelsolutionstomassiveproblemswithlibradosandsharedobjectclasses.Chooseintelligentparametersforanerasurecodedpoolandsetitup.ConfiguretheBluestoresettingsandseehowtheyinteractwithdifferenthardwareconfigurations.KeepCephrunningthroughthickandthinwithtuning,monitoringanddisasterrecoveryadvice.AbouttheAuthorNickFiskisanITspecialistwithastronghistoryinenterprisestorage.Havingworkedinavarietyofrolesthroughouthiscareer,hehasencounteredawidevarietyoftechnologies.In2012,Nickwasgiventheopportunitytofocusmoretow

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。