Autonomic Communication PDF
Preview Autonomic Communication
EEAE utonomic Communication Athanasios V. Vasilakos (cid:127) Manish Parashar Stamatis Karnouskos (cid:127) Witold Pedrycz Editors Autonomic Communication Editors Athanasios V. Vasilakos Manish Parashar Department of Computer Department of Electrical and Telecommunications Engineering and Computer Engineering University of Western Macedonia Rutgers University Agios Dimitrios Park 94 Brett Road 501 00 Kozani Piscataway, NJ 08854-8058 Greece USA [email protected] [email protected] Stamatis Karnouskos Witold Pedrycz SAP AG Corporate Research Centre Department of Electrical Vincent-Prießnitz-Str. 1 and Computer Engineering 76131 Karlsruhe University of Alberta Germany 9107 116 Street [email protected] Edmonton, AB T6G 2V4 Canada [email protected] ISBN978-0-387-09752-7 e-ISBN978-0-387-09753-4 DOI10.1007/978-0-387-09753-4 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009931557 ©SpringerScience + BusinessMedia,LLC2009 Allrightsreserved. Thisworkmaynotbetranslatedorcopiedinwholeorinpartwithoutthewritten permissionofthepublisher(SpringerScience+BusinessMedia,LLC,233SpringStreet,NewYork,NY 10013,USA),exceptforbriefexcerptsinconnectionwithreviewsorscholarlyanalysis.Useinconnection withanyformofinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdevelopedisforbidden. Theuseinthispublicationoftradenames,trademarks,servicemarks,andsimilarterms,eveniftheyare notidentifiedassuch,isnottobetakenasanexpressionofopinionastowhetherornottheyaresubject toproprietaryrights. Printedonacid-freepaper Springer is part of Springer Science+Business Media (www.springer.com) Foreword As informationtechnologybecomes increasingly more sophisticated and capable, repeatedstudieshavedemonstratedthatthishasbeenmatchedbyanincreasingcost andcomplexityinconfiguring,managingandservicingsuchsystems. Despitetheobviousbenefitsinperformance&scalabilitythathasbeenprovided bytheongoingevolutionoffasterCPUs,increasednetworkbandwidthandstorage capacity,thecostofdynamicallymanagingthe hardware,softwareandinfrastruc- turehascontinuedtoriseatanalarmingrate. Autonomic Computing emerged at the dawn of the 21st century out of cross- industry and academic research and development in simplifying administration, configuration,deploymentandmanagementofITsystems. Driven by a need to address what many had seen as a growing crisis in the IT industry for increased visibility, control and automation of complex IT systems, autonomic computing focuses upon providing embedded and integrated manage- mentsystemsthatenableself-healing,self-configuration,self-optimizationandself- protection. Aswe moveintotheeraofenergy-awareITsystemsandcloud-computing,the needsfor autonomicsystems have continueto evolve. Recently there has been an increased awareness of the need for efficient utilization of data centers associated with the changingeconomicsof energysupplyand the politicsofclimate change. Optimal use of communications infrastructure and the associated middleware has becoming increasingly critical. Embedded autonomic capabilities to provide self- managementhasbecomecriticalindeliveringnewdynamicinfrastructureinIT. Increased use of the Internet has led to global-scale broad-band networking. When coupled with adoption of wireless communications and mobile computing byconsumersandenterprises,theneedforself-managingnetworkservicesandau- tonomiccommunicationsbecomesmorepressing.Thesamenetworkinfrastructure isbeingsharedacrossmoreandmoreservices.Enterprises,consumersandgovern- mentsexpecttohavecommunicationsystemsthatcanautonomicallyrecoverfrom failuresorreconfigurethemselvestocopewithincreaseddemand. Thosesamenetworkservicesarealsobeingusedtoprovideinterfacesontotradi- tionalphysicalinfrastructure:Smartutilitygridsforefficientproductionanddistri- v vi Foreword butionofgas,waterandelectricity,wirelesshome-metering,remotewirelesstrack- ingofparcels,shippingcontainersandwirelessmonitoringoftheenvironmentand climatechange.Theseareallexpectedtobecomethenormasweprogresstowards the’SmartPlanet’. This volume is a timely overview of the evolution of autonomic computing to communications,networkingandsensorsystems,andprovidesdetailedinsightsinto themulti-disciplinaryresearchtopicsthatcomprisethefieldofAutonomicComput- ingandCommunications. April2009 Dr.MattEllis VicePresident AutonomicComputing,IBM Preface Theemergingworldispervasiveandstrivestowardsintegratingpeople,technology, environmentandknowledge.Thisemergingvisionsupportsapproachesthatsetthe user at the center of attention, while technology becomes invisible, hidden in the naturalsurroundings,butstillfunctional,autonomous,self-adaptive,availablewhen needed,andinteractive. Achievingthisvisionrequiresinnovativecommunicationarchitecturesandser- vices. Communication/networkingsolutionsshould become task- and knowledge- driven, enabling a service oriented, requirement and trust based development of communicationinfrastructure.Thegrowingcomplexityofcontrolrequiresincreas- ingly distributed and self-organizingstructures, relyingon simple and dependable elementsthatareabletocollaboratetodevelopsophisticatedbehaviors,andthatcan adapttoanevolvingsituationwherenewresourcescanbecomeavailable,adminis- trativedomainscanchangeandeconomicmodelscanvary. Thenetworkingandseamlessintegrationofconcepts,technologiesanddevices inadynamicallychangingenvironmentposesmanychallengestotheresearchcom- munity.Thereremainsuchcrucialissuesasinteroperability,programmability,man- agement,openness,reliability,performance,contextawareness,intelligence,auton- omy,security,privacy,safety,semantics,etc.However,theoverallscale,complex- ity, heterogeneityand dynamicsof these networkedenvironments,together,result inessentialmanagementchallengeswhichclearlygobeyondcurrentparadigmsand practices,andneedafundamentallynewapproach. AutonomicCommunicationissuchanapproach.Itisinspiredbybiologicalsys- temsandenvisionscommunicationsystemsthatarelargeself-managingwhichcan organize, configure, optimize, protect and heal themselves with minimal involve- mentofhumanadministrators. Thiseditedvolumeexploresconceptualmodelsandassociatedtechnologiesthat willhelprealizethevisionofautonomiccommunication,wheredevicesandappli- cationsseamlesslyinterconnect,intelligentlycooperateandautonomouslymanage themselves,andasaresult,enablethebordersofvirtualandrealworldtovanishor becomesignificantlyblurred.Thechapterscontributingtotheeditedvolumearethe genuinetestimonytothechallengesandprospectsofthisrapidlygrowingarea. vii viii Preface Thevolumeiscomposedof14chapterscoveringawiderangeofissuerelatedto autonomiccommunicationandareorganizedinto3partsaslistedbelow. AutonomicCommunicationInfrastructure • AutonomicCommunicationServicesandMiddleware • ApplicationstoAd-Hoc(Sensor)NetworksandPervasiveSystems • Part I – Autonomic CommunicationInfrastructure: Part I focuses on various aspectsofautonomiccommunicationinfrastructureandcontains4chapters.Chapter 1titled“Bio-inspiredAutonomicStructures:amiddlewareforTelecommunications Ecosystems”investigatesabio-inspiredautonomicmiddlewarecapableofleverag- ingtheassetsoftheunderlyingnetworkinfrastructurewhilst,atthesametime,sup- porting the development of future Telecommunications and Internet Ecosystems. Chapter2titled“Social-basedautonomicroutinginopportunisticnetworks”inves- tigates contex-aware routing in opportunistic autonomic infrastructures under the prism of peoples’ social behavior. Chapter 3 titled “A Collaborative Knowledge Plane for Autonomic Networks” looks at a knowledge plane architecture aiming atself-adaptationandself-organization.Chapter4titled“ARateFeedbackPredic- tive ControlSchemeBasedonNeuralNetwork andControlTheoryforAutonomic Communication”tackles the difficultyin designingan efficientcongestion control schemebyusingbackpropagationneuralnetworks. PartII–AutonomicCommunicationServicesandMiddleware:PartIIfocuses on specific autonomic communication services and middleware frameworks, and contains5chapters.Chapter5titled“HoveringInformation–Self-OrganisingInfor- mationthatFindsitsOwnStorage”investigatesahoveringinformationmodeland reportson simulationsperformedusing replicationand cachingalgorithms.Chap- ter6titled“TheCASCADASFrameworkforAutonomicCommunications”presents a prototype distributed component-ware framework for autonomic and situation- aware communicationand demonstratesit via a Pervasive BehaviouralAdvertise- ment scenario. Chapter 7 titled “Autonomic Middleware for Automotive Embed- ded Systems” describesan advancedautonomicplatform-independentmiddleware frameworkfocusedonautomotiveembeddedsystemswherehighflexibilityandau- tomatic run-timereconfigurationis needed.Chapter8 titled “SocialOpportunistic Computing:DesignforAutonomicUser-CentricSystems”focusesonthediffusion of data in autonomic computingenvironmentsand the way the social attitudes of mobile users impact their design. Chapter 9 titled “Programming and Validation Techniques for Reliable Goal-driven Autonomic Software” investigates time and concurrency which are the most critical notions of complex software interactions inautonomousflightsystems. PartIII – Applicationsto Ad-Hoc (Sensor) Networks and Pervasive Systems: PartIIIfocusesontheapplicationsofautonomiccommunicationtoad-hoc(sensor) networks and pervasive systems, and contains 5 chapters. Chapter 10 titled “Au- tonomicCommunicationinPervasive MultimodalMultimediaComputingSystem” focuseson autonomiccommunicationprotocolsinvolvedin thedetectionof inter- action context and the multimodal computing system’s corresponding adaptation. Chapter 11 titled “Self-healing for Autonomic Pervasive Computing” focuses on Preface ix theself-healingaspectsofautonomicpervasivecomputinganddemonstratesitasa serviceintheMiddlewareAdaptabilityforResourcediscovery,Knowledgeusabil- ity,andSelf-healingplatform.Chapter12titled“Map-basedDesignforAutonomic WirelessSensorNetworks”presentsanapproachthatexploitsthespatialcorrelation ofsensorreadingsandbuildsamodelthatabstractsfromlow-levelcommunication issues and supports general applications by allowing for efficient event detection, prediction and queries. Chapter 13 titled “An Efficient, Scalable and Robust P2P Overlay for Autonomic Communication” focuses on the applicability of P2P sys- temsinautonomiccommunicationdomain.Chapter14titled“AutonomicandCo- evolutionary Sensor Networking with BiSNET/e” proposes a biologically inspired architecturethatallowswirelesssensornetworkapplicationstosimultaneouslysat- isfy conflicting operationalobjectives by adapting to dynamic network conditions (e.g.,networktrafficandnode/linkfailures)throughevolution. Oursincerethanksgototheauthorswhohavecontributedtothiseditedvolume by sharing their most recent research findings and expertise. The anonymous re- viewers deserve our thanksfor their constructivecriticism, helpfulcommentsand in-depth insights. Finally, we would like to express our gratitude to Springer for agreeingtopublishthisvolumeandespeciallytoValerieSchofieldandhereditorial teamthatweinteractedwithonacontinuousbasis.Wedohopethecommunityof researchersandpractitionerswillfindthecontentofthisvolumeinspiring,insightful andenjoyable. April2009 AthanasiosVasilakos ManishParashar StamatisKarnouskos WitoldPedrycz Contents PartI AutonomicCommunicationInfrastructure Bio-inspired Autonomic Structures: a middleware for TelecommunicationsEcosystems ..................................... 3 AntonioManzalini,RobertoMinervaandCorradoMoiso 1 Introduction.............................................. 4 2 StateofArt .............................................. 6 2.1 AutonomicFrameworks............................ 6 2.2 InteractionAlgorithms ............................. 9 3 Bio-inpiredAutonomicStructures ........................... 10 3.1 ConceptofAutonomicStructures .................... 11 3.2 BASmiddleware .................................. 12 3.3 DataComponentsinteractions:primitives ............. 14 3.4 Componentsinteractions:mechanismsandalgorithms... 15 4 Engineerself-organization.................................. 18 4.1 GameTheoryforcross-layerdesign.................. 20 4.2 Auctionsforoptimizedresourceallocation ............ 22 5 Applicationscenarios...................................... 23 5.1 Self-ManagementforTelecommunicationsNetworks ... 23 5.2 CloudComputing ................................. 24 5.3 HomeNetworking................................. 25 6 Conclusions.............................................. 27 References..................................................... 28 Social-basedautonomicroutinginopportunisticnetworks .............. 31 ChiaraBoldrini,MarcoConti,AndreaPassarella 1 Introduction.............................................. 32 2 Theopportunisticnetworkingconceptanditsapplications ....... 33 2.1 Opportunisticnetworkingcasestudiesandapplications.. 35 3 Social-basedmobility...................................... 36 3.1 CMMandHCMM:functionaldescription............. 37 xi xii Contents 3.2 HCMMvs.CMM:ControllingNodePositions......... 40 4 Routinginopportunisticnetworks ........................... 43 4.1 Context-obliviousrouting........................... 43 4.2 Partiallycontext-awarerouting ...................... 44 4.3 Fullycontext-awarerouting ......................... 46 4.4 TheHistory-basedOpportunisticRoutingprotocol...... 47 5 Performanceofopportunisticroutingapproachesundersocial mobilitypatterns.......................................... 48 5.1 Performanceevaluationstrategy ..................... 48 5.2 Impactofcollectivegroups’movements(reconfigurations) 50 5.3 ImpactofUserSociability .......................... 54 5.4 BreakingClosedGroups............................ 59 6 Conclusions.............................................. 62 References..................................................... 65 ACollaborativeKnowledgePlaneforAutonomicNetworks ............. 69 MaïssaMbayeandFrancineKrief 1 Introduction.............................................. 69 2 AutonomicNetworking .................................... 71 2.1 Basicconcepts.................................... 71 2.2 RelatedWork..................................... 71 3 Collaborativeknowledgeplanearchitecture ................... 73 3.1 Architectureoverview.............................. 73 3.2 BasicConcepts ................................... 74 3.3 Knowledgeplanebuildingblocks .................... 76 4 Self-adaptationloop ....................................... 78 4.1 Machinelearningalgorithmforself-adaptation......... 78 4.2 StudyCase:self-adaptationofaDiffServrouter ........ 79 5 Collaborativeloop ........................................ 83 5.1 SituatedViewandBasicconcepts.................... 83 5.2 SituatedKnowledgesharingalgorithm................ 86 5.3 Performanceandguarantees ........................ 88 6 Conclusion............................................... 90 References..................................................... 90 ARateFeedbackPredictiveControlSchemeBasedonNeuralNetwork andControlTheoryforAutonomicCommunication.................... 93 NaixueXiong,AthanasiosV.Vasilakos,LaurenceT.Yang,FeiLong,Lei Shu,andYingshuLi 1 Introduction.............................................. 94 2 CongestionControlModel.................................. 95 2.1 ThePredictiveControlModelofaBottleneckBuffer.... 95 3 ThePredictiveControlTechnique............................ 98 3.1 TheBPNeuralNetworkArchitecture................. 98 3.2 Multi-stepNeuralPredictiveTechnique ............... 98 4 TheSimulationResults .................................... 99
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