简介：自愿的云是云计算的一个新范例。它与一些好预备的云一起提供一种其他的选择。然而，在不明确的时间跨越那参加者份额他们在自愿的云的计算资源，有一些，质问问题，即，变化，在能力下面并且低利益。在这份报纸，体系结构首先基于Bittorrent协议被建议。在这体系结构，资源能从保留的例子市场被保留或请求并且能在一个短圆与更低的价格被存取。实际上，这些资源能填满不适当的资源在自愿的云分享并且减轻变化和在能力下面问题。然后，每个节点的差错率被用来评估它的分享的时间的无常。由利用一个线性预言模型，它被被用于评估系统的计算能力的一个分发函数启用。而且，费用优化问题被调查，一个计算方法被介绍在自愿的云解决低利益的问题。最后，系统表演被模拟的二个集合验证。并且试验性的结果为资源保留优化显示出我们的计算方法的有效性。

简介：Becausetrustisregardedasanessentialsecuredrelationshipwithinadistributednetworkenvironment,selectingservicesovertheInternetfromtheviewpointoftrusthasbeenamajortrend.CurrentresearchabouttrustmodelandevaluationinthecontextofWebservicesdoesnotrationallyandaccuratelyreflectsomeessentialcharacteristicsoftrustsuchassubjectiveuncertaintyanddynamism.Inthispaper,weanalyzesomeimportantcharacteristicsoftrust,andsomekeyfactorsthataffectthetrustrelationintheWebserviceenvironment.Accordingly,weproposeatrustmodelbasedonCloudModeltheorytodescribethesubjectiveuncertaintyoftrustfactors.Atime-relatedbackwardcloudgenerationalgorithmisgiventoexpressthedynamismoftrust.Furthermore,accordingtothetrustmodelandalgorithm,aformalizedcalculationapproachisprovidedtoevaluatethetrustdegreeofservicesrequestorsinproviders.Ourexperimentshowsthattheevaluationoftrustdegreecaneffectivelysupporttrust-decisionsandprovideahelpfulexploitationforselectingservicesbasedontheviewpointoftrust.

简介：Inthispaper,ageometry-basedpointcloudreductionmethodisproposed,andareal-timemobileaugmentedrealitysystemisexploredforapplicationsinurbanenvironments.Weformulateanewobjectivefunctionwhichcombinesthepointreconstructionerrorsandconstraintsonspatialpointdistribution.Basedonthisformulation,amixedintegerprogrammingschemeisutilizedtosolvethepointsreductionproblem.Themobileaugmentedrealitysystemexploredinthispaperiscomposedoftheofttineandonlinestages.Attheofflinestage,webuildupthelocalizationdatabaseusingstructurefrommotionandcompressthepointcloudbytheproposedpointcloudreductionmethod.Whileattheonlinestage,wecomputethecameraposeinrealtimebycombininganimage-basedlocalizationalgorithmandacontinuousposetrackingalgorithm.Experimentalresultsonbenchmarkandrealdatashowthatcomparedwiththeexistingmethods,thisgeometry-basedpointcloudreductionmethodselectsapointcloudsubsetwhichhelpstheimage-basedlocalizationmethodtoachievehighersuccessrate.Also,theexperimentsconductedonamobileplatformshowthatthereducedpointcloudnotonlyreducesthetimeconsumingforinitializationandre-initialization,butalsomakesthememoryfootprintsmall,resultingascalableandreal-timemobileaugmentedrealitysystem.

简介：数据安全为云计算是领先的担心和主要挑战之一。这个问题与云计算的发展正在变得越来越严肃。然而，存在保存隐私的数据分享技术也没能阻止隐私的漏或招致信息损失的巨大的数量。在这份报纸，我们建议一种新奇技术，作为基于连接的匿名模型称为，它与有一种尺寸的伪标识符组(QI组)一起完成K匿名不到K。同时，半同质的归纳被介绍对同质的归纳招致的攻击。为了实现基于连接的anonymization，当模特儿，我们建议一个简单还有效的启发式的本地人重新代码方法。真实数据集的广泛的实验也被进行证明实用程序被我们的途径显著地与最先进的方法相比改进了。

简介：与WiFi和3G/4G的快速的发展，人们趋于在移动设备上看录像。这些设备是无所不在的，但是有小存储器缓冲录像。作为结果，与传统的计算机相对照，这些设备加重内容供应商的网络压力。以前的研究使用CDN解决这个问题。但是它出租空间不能动态地在被调整的静态的租借机制让运作的费用高飞并且与不兼容动态地录像交货。在我们的学习，基于从Tencent录像的用户行为的彻底的分析，一个流行中国联机录像份额平台，我们识别二关键用户行为。第一，在一样的区域的大量用户趋于看一样的录像。第二，录像的流行分发符合Pareto原则，即，20%流行录像拥有的顶80%所有录像交通。把这些观察变成银子弹，我们在需求系统(CPA-VoD)上建议并且实现一个新奇帮助云、帮助同伴的录像。在系统，我们在象同伴群的一样的区域，并且在一样的同伴群组织用户，用户们能由分享他们的缓冲录像提供录像给另外的用户。而且，我们在云服务器缓冲10%很流行的录像进一步减轻网络压力。我们选择云服务者因为出租空间能动态地被调整，缓冲录像。根据从Tencent录像的真实数据集上的评估，CPA-VoD最优地减轻网络压力和操作费用，当仅仅20.9%交通被内容供应商满足时。

简介：Avaya公司近期宣布与Salesforce服务云达成全球战略联盟合作．以提供能够更紧密地整合双方联络中心产品的解决方案。面临向Web和云应用迁移的趋势．Avaya与Salesforce推出的整合解决方案将帮助公司在不干扰或极少干扰当前运行的情况下．快速推进战略的执行。当需要更多个性化帮助时．客户可以方便地从移动自助服务快速切换到他助服务——例如使用聊天机器人程序期间．

简介：近日，微软和联想在北京举办签约仪式．正式宣布联想成为中国首家微软CloudOS战略合作伙伴。未来，联想将使用基于微软WindowsServer及Hyper—V、

简介：Withthegrowingpopularityofcloud-baseddatacenternetworks(DCNs),taskresourceallocationhasbecomemoreandmoreimportanttotheefficientuseofresourceinDCNs.Thispaperconsidersprovisioningthemaximumadmissibleload(MAL)ofvirtualmachines(VMs)inphysicalmachines(PMs)withunderlyingtree-structuredDCNsusingthehosemodelforcommunication.Thelimitationofstaticloaddistributionisthatitassignstaskstonodesinaonce-and-for-allmanner,andthusrequiresaprioriknowledgeofprogrambehavior.Toavoidloadredistributionduringruntimewhentheloadgrows,weintroducemaximumelasticityscheduling,whichhasthemaximumgrowthpotentialsubjecttothenodeandlinkcapacities.Thispaperaimstofindtheschedulewiththemaximumelasticityacrossnodesandlinks.Wefirstproposeadistributedlinearsolutionbasedonmessagepassing,andwediscussseveralpropertiesandextensionsofthemodel.Basedontheassumptionsandconclusions,weextendittothemultiplepathscasewithafattreeDCN,anddiscusstheoptimalsolutionforcomputingtheMALwithbothcomputationandcommunicationconstraints.Afterthat,wepresenttheprovisionschemewiththemaximumelasticityfortheVMs,whichcomeswithprovableoptimalityguaranteeforafixedflowschedulingstrategyinafattreeDCN.Weconducttheevaluationsonourtestbedandpresentvarioussimulationresultsbycomparingtheproposedmaximumelasticschedulingschemeswithothermethods.Extensivesimulationsvalidatetheeffectivenessoftheproposedpolicies,andtheresultsareshownfromdifferentperspectivestoprovidesolutionsbasedonourresearch.

简介：Cloudcomputingisatechnologythatprovidesuserswithalargestoragespaceandanenormouscomputingpower.However,theoutsourceddataareoftensensitiveandconfidential,andhencemustbeencryptedbeforebeingoutsourced.Consequently,classicalsearchapproacheshavebecomeobsoleteandnewapproachesthatarecompatiblewithencrypteddatahavebecomeanecessity.Forprivacyreasons,mostoftheseapproachesarebasedonthevectormodelwhichisatimeconsumingprocesssincetheentireindexmustbeloadedandexploitedduringthesearchprocessgiventhatthequeryvectormustbecomparedwitheachdocumentvector.Tosolvethisproblem,weproposeanewmethodforconstructingasecureinvertedindexusingtwokeytechniques,homomorphicencryptionandthedummydocumentstechnique.However,1)homomorphicencryptiongeneratesverylargeciphertextswhicharethousandsoftimeslargerthantheircorrespondingplaintexts,and2)thedummydocumentstechniquethatenhancestheindexsecurityproduceslotsoffalsepositivesinthesearchresults.Theproposedapproachexploitstheadvantagesofthesetwotechniquesbyproposingtwomethodscalledthecompressedtableofencryptedscoresandthedoublescoreformula.Moreover,weexploitasecondsecureinvertedindexinordertomanagetheusers'accessrightstothedata.Finally,inordertovalidateourapproach,weperformedanexperimentalstudyusingadatacollectionofonemilliondocuments.Theexperimentsshowthatourapproachismanytimesfasterthananyotherapproachbasedonthevectormodel.

简介：Whilecloud-basedBPM(BusinessProcessManagement)showspotentialsofinherentscalabilityandexpenditurereduction,suchissuesasuserautonomy,privacyprotectionandefficiencyhavepoppedupasmajorconcerns.Usersmayhavetheirownrudimentaryorevenfull-edgedBPMsystems,whichmaybeembodiedbylocalEAIsystems,attheirend,butstillintendtomakeuseofcloud-sideinfrastructureservicesandBPMcapabilities,whichmayappearasPaaS(Platform-as-a-Service)services,atthesametime.Awholebusinessprocessmaycontainanumberofnon-compute-intensiveactivities,forwhichcloudcomputingisover-provision.Moreover,someusersfeardataleakageandlossofprivacyiftheirsensitivedataisprocessedinthecloud.Thispaperproposesandanalyzesanovelarchitectureofcloud-basedBPM,whichsupportsuser-enddistributionofnon-compute-intensiveactivitiesandsensitivedata.Anapproachtooptimaldistributionofactivitiesanddataforsyntheticallyutilizingbothuser-endandcloud-sideresourcesisdiscussed.Experimentalresultsshowthatwiththehelpofsuitabledistributionschemes,dataprivacycanbesatisfactorilyprotected,andresourcesonbothsidescanbeutilizedatlowercost.