Autonomic Web Processes Presenter: Amit Sheth METEOR-SMETEOR-S project, LSDIS LabLSDIS Lab Computer Science, University of Georgia Presentation of the Vision Paper (Invited): Kunal Verma Kunal Verma and Amit Sheth. Autonomic Web Processes. In Proceedings of the Third International Conference on Service-oriented Computing (ICSOC 2005), - Vision Paper (invited), LNCS 3826, Springer Verlag, 2005, pp Amit Sheth

Introduction Growing need for creating more adaptive/dynamic process frameworks IBM’s vision of autonomic computing lays foundation of adaptive/self managing systems Our vision seeks to elevate Autonomic Web Processes from the infrastructure to the process level

Autonomic Nervous System Responsible for maintaining constant internal environment of human body by controlling involuntary functions like: –digestion, respiration, perspiration, and metabolism Divided into two subsystems: –Sympathetic and parasympathetic

Autonomic Nervous System Sympathetic –providing responses and energy needed to cope with stressful situations such as fear or extremes of physical activity Increases blood pressure, heart rate, and the blood supply to the skeletal muscles at the expense of the gastrointestinal tract, kidneys, and skin Parasympathetic –Brings normalcy in between stressful periods which lowers the heart rate and blood pressure, diverts blood back to the skin and the gastrointestinal tract

An Example

Autonomic Computing Autonomic Computing is an initiative started by IBM in 2001 Aims to make systems that simulate the autonomic nervous system by having the ability to be more self managing Objective to let user specify high level policies and then the system should be able to manage itself

Autonomic Computing - properties Infrastructural Components with Self-CHOP properties –Self Configuring –Self Healing –Self Optimizing –Self Protecting Examples –Self Adaptive Middleware –Self Healing Databases –Autonomic Server Monitoring

Autonomic Web Processes (AWPs) Natural Evolution of Autonomic Computing from infrastructure to Web process level –Web processes are Web services based workflows Require Web process frameworks that have the following properties –Support Self-CHOP properties –Policy based interaction with other components –Based on open standards (WS technologies) Based on the synergy between a number of broad fields –Autonomic Computing, Web Services, Service Oriented Architectures, Operations Research, Control Theory, Semantic Web, Dynamic and Adaptive Web Processes/Workflows

Use Case Supply Chain of computer manufacturer Self Configuring: Can the process be configured based on constraints and policies Self Healing: Can the process recover from physical and logical failures Self Optimizing: Can the process reconfigure itself in case of changes in environment.

Architecture

Self Configuring Depending on the scope, configuration may include –Creation of process (manual/semi-automatic/planning) –Discovery of partners (internal/external registry) –Negotiation (manual/automated) –Constraint Analysis (quantitative/logical/hybrid) Require representation of: –Functional semantics for discovery –Non-functional semantics for constraint analysis – constraints, policies, SLAs

Self Configuring Constraint based Configuration Configured Process

Self Healing Process must be able to recover from –Failures of physical components like services, processes, network –Logical failures like violation of SLA constraints/Agreements Delay in delivery, partial fulfillment of order Require representation of execution semantics –Physical and Logical Exceptions and recovery paths

Self Healing – Creating Execution Graph of a SM Actions Events Flags

Self Healing Execution Graph- Generated from Operations, Events and Flags 5 Flags, thus 2 5 = 32 possible states (only 8 reachable states) One proposed approach: Use Markov Decision Processes to choose optimal actions S1- Ordered = True (All other flags false) S4 - Ordered = True and Received = false S5-Ordered = True and Delayed = false ---Transition due to action - - Exogenous events (example probabilities of occurrence of the events conditioned on the states) K. Verma, P. Doshi, K. Gomadam, J. Miller, A. Sheth, Optimal Adaptation in Autonomic Web Processes with Inter-Service Dependencies, LSDIS Lab, Technical Report, November 2005

Self Optimizing Process must be able to reconfigure itself with changes in environment –Fluctuations in currency exchange rates of overseas suppliers –New discounts or cheaper suppliers available Must choose between long term and short term benefits This requires both functional and non-functional semantics

Self Optimizing Sympathethic Policy Reconfigure process for immediate gain May including canceling order from previous Supplier Change in Currency Rate beyond threshold Parasympathethic Policy Consider long term supplier relationship

Model Functional and Data Semantics –Service (WSDL-S)[1] Non-Functional Semantics –Policies (Semantically Annotated Policy)[2] Business Level Policies, Process Level Policies, Instance Level Policies Individual Component Level Policy –Agreements (SWAPS) [3] Execution Semantics –State based representation of exceptions/failures –Process (BPEL + Semantic Templates) [4] Ontologies –Domain Specific Ontologies, –Domain Independent/Upper Ontologies AWP Property/ Type of Semantics Self Configuring Self Healing Self Optimizing Data Functional Non- Functional Execution [1] Web Service Semantics – WSDL-S, W3C Member Submission., [2] K. Verma, R. Akkiraju, R. Goodwin, Semantic matching of Web service policies, SDWP, 2005Semantic matching of Web service policies [3] N. Oldham, K. Verma, A. Sheth, Semantic WS-Agreement Partner Selection [4] K. Sivashanmugam, J. Miller, A. Sheth, and K. Verma, Framework for Semantic Web Process Composition, IJEC, 2004Framework for Semantic Web Process Composition

AWPs vs. Autonomic Computing Autonomic Computing Autonomic Web Processes DatabasesNetworksServers Autonomic IT Infrastructure Self Configuring: Lower IT cost on maintenance and deployment. Self Healing: Lower human involvement in problem detection, analysis and solving. Self Optimizing: Better SLAs to customers of the IT infrastructure. Business Processes Self Configuring: Processes configured with respect to business policies. Self Healing: Quick responses to failures, leading to large savings in cost. Self Optimizing: Environment changes lead to reconfiguration to a lower cost process.

Conclusions The Vision: –AWPs seek to create next generation of Web process technology Current Work: –Initial work at UGA on using MDPs for adaptation –IBM work on WSDM for autonomic Web services –Paolo Traverso et al. - Autonomic Composition of Business Processes The Future: –We invite researchers from SOA, Web services, AI, multi- agents, operations research, control theory to contribute to this vision –Dagstuhl-Seminar: Autonomic Web Services and Processes (possibly in August 2006) Contact: Paolo Traverso, Amit Sheth