Autonomic Applications for Pervasive Environments

Manish ParasharWINLAB/TASSLECE, Rutgers Universityhttp://www.caip.rutgers.edu/TASSLAck: NSF (CAREER, KDI, ITR, NGS), DoE (ASCI)

Pervasive Computing: Smaller/Cheaper/Faster/Connected .Explosive growth in computation, communication, information and integration technologiescomputing is ubiquitous, pervasive communication is/will bePervasive anytime-anywhere access environmentsubiquitous access to information via PCs, PDAs, Cells, smart appliances, etc. (billions of devices, millions of users)peers capable of producing/consuming/processing information at different levels and granularitiesembedded devices in clothes, phones, cars, mile-markers, traffic lights, lamp posts, refrigerators, medical instruments Pervasive on demand computational/storage resources, servicesthe Grid

Example scenario : DARPA IXO, A Rapidly Expanding Universe of Sensors, Weapons, and Platforms

The bad news Unprecedentedscalescomplexityheterogeneitydynamism and unpredictabilitylack of guaranteesThe increasing system complexity is reaching a level beyond human ability to design, manage and secure programming environments and infrastructure are becoming unmanageable, brittle and insecureA fundamental change is required in how applications are formulated, composed and managedautonomic components, dynamic compositions, opportunistic interactions, virtual runtime,

Autonomic Computing?Nature has evolved to cope with scale, complexity, heterogeneity, dynamism and unpredictability, lack of guaranteesself configuring, self adapting, self optimizing, self healing, self protecting, highly decentralized, heterogeneous architectures that work !!!e.g. the human body the autonomic nervous system tells you heart how fast to beat, checks your bloods sugar and oxygen levels, and controls your pupils so the right amount of light reaches your eyes as you read these words, monitors your temperature and adjusts your blood flow and skin functions to keep it at 98.6Fcoordinates - an increase in heart rate without a corresponding adjustment to breathing and blood pressure would be disastrousis autonomic - you can make a mad dash for the train without having to calculate how much faster to breathe and pump your heart, or if youll need that little dose of adrenaline to make it through the doors before they closecan these strategies inspire solutions?e.g. FlyPhones, AORO/AutoMate, ROC, ELiza, etc.of course, there is a costlack of controllability, precision, guarantees, comprehensibility, PS: its not AI duplication of human thought is not the ultimate goal

Autonomic LivingAutonomic living: autonomic peers opportunistically interact, coordinate and collaborate to satisfy goals?scenarios (everyday, b2b coordination, crisis management, homeland security, )your car in route to the airport estimates that given weather (from meteorological beacons), road conditions (from on-coming cars), traffic patters (from the traffic light), warns that you will miss your flight and you will be better off taking the train the station is coming up do you want to rebook ?in a foreign country, your cell phone enlists a locally advertised GPS and translation service as you try to get directionsyour clock/PDA estimates drive time to your next appointment and warns you appropriatelyyour eye glasses sends your current prescription as you happen to drive past your doctor or your PDA collects prices for the bike you promised yourself as you drive around

Project overviewInvestigate fundamental architectures and software paradigms/technologies associated with development of autonomic and pervasive applicationspervasive computing system design, based on a challenging application scenario: autonomic livingmiddleware infrastructures and servicesprogramming model based on local constraints, opportunistic interactions and consensus based coordinationcluster computing model for loosely coupled ac-hoc scenariosecurity/trust architecture and protocols for the abovead-hoc wireless (sensor) network protocol optimizationsautonomic living system prototypeThe group: M. Ott, M. Parashar, D. Raychaudhury, W. Trappe Y, Zhang

AutoMate: Enabling Autonomic ApplicationsObjective:Investigate key technologies to enable the development of autonomic Grid applications that are context aware and are capable of self-configuring, self-composing, self-optimizing and self-adapting. Research Issues:Definition of Autonomic Components:definition of programming abstractions and supporting infrastructure that will enable the definition of autonomic componentsautonomic components provide enhanced profiles or contracts that encapsulate their functional, operational, and control aspects Dynamic Composition of Autonomic Applications:mechanisms and supporting infrastructure to enable autonomic applications to be dynamically and opportunistically composed from autonomic componentscompositions will be based on policies and constraints that are defined, deployed and executed at run time, and will be aware of available Grid resources (systems, services, storage, data) and components, and their current states, requirements, and capabilitiesAutonomic Middleware Services:design, development, and deployment of key services on top of the Grid middleware infrastructure to support autonomic applicationsa key requirements for autonomic behavior and dynamic compositions is the ability of the components, applications and resources (systems, services, storage, data) to interact as peers

Opportunistic InteractionsInteractions based on local goals and objectiveslocal goals and objectives are defined as constraints to be satisfiedconstraints can updated and new constraints can defined at any timeDynamic and ad-hocinteractions use semantic messaging based on proximity, privileges, capabilities, context, interests, offerings, etc.Opportunisticconstraints are long-term and satisfied opportunistically (may not be satisfied)Probabilistic guarantees and soft stateno explicit synchronization interaction semantics are achieved using feedback and consensus building

Opportunistic Interactions

For ExampleAutomata using opportunistic interactionsGame of Life simulation using sensor networksSensors as Sharks and FishesShark/Fish behavior encoded as constraintsOpportunistic and dynamic interactions based on proximity and interestsSemantic messaging based on interest/offer profilesInteraction semantics built on local behavior (no synchronization)Inconsistencies resolved and consensus built incrementally using probabilistic feedback and gossipingShark thinks it has eaten the fish, fish thinks it got away !Indirect interactions Fish warns another fish of an approaching sharkAggregation Shark does not attack fishes if they are in a school of 10 or moreDynamic goals and constraintsConstraint satisfactions is ongoing and probabilistic not absoluteConstraints can be updated, removed or new constraints to be defined.Possible a result of constraint satisfactionShark is full after eating 4 fishes

SummaryAutonomic applications are necessary to address scale/complexity/heterogeneity/dynamism/reliability challengesAutonomic living: enable autonomic peers to opportunistically interact, coordinate and collaborate to satisfy goalsprogramming/interaction models, messaging substrates, ad hoc clustering mechanisms, security and trust, ad hoc networksMore Informationhttp://automate.rutgers.edu

Autonomic Computing Workshop/TutorialAutonomic Computing Workshop In conjunction with the Twelfth International Symposium on High Performance Distributed Computing (HPDC-12), June 25th, 2003 in Seattle Washington.www.caip.rutgers/edu/ams2003Autonomic Computing Tutorial Global Grid Forum (GGF), June 22nd, 2003 in Seattle Washington.http://automate.rutgers.edu

OverviewOverviewIndividual systems approaching human limits