autonomic web processes presenter: amit sheth meteor-smeteor-s project, lsdis lablsdis lab computer...
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Autonomic Web ProcessesPresenter: Amit Sheth
METEOR-S project, LSDIS LabComputer Science, University of Georgia
Presentation of the Vision Paper (Invited):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. 1-11.
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
http://www.research.ibm.com/autonomic/
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
http://www.nda.ox.ac.uk/wfsa/html/u05/u05_010.htm
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
http://www.sirinet.net/~jgjohnso/nervous.html
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.
receive
orderMB
orderRAM
Wait for Delivery
Architecture
Process Manager
(PM)
Service Manager
(SM)
Configuration Manager
(CM)
Process Instances
Partner Service
Configuration Module (ILP,
SWRL)
Resources Layer
Autonomic Layer
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
receive
orderMB
orderRAM
Wait for Delivery
Order
Return
Cancel
MBSupplier WS (M2)
Order
Return
Cancel
RAMSupplier WS (R1)
PM
SM1
SM2ILP
SolverCM SWRL
Reasoner
Constraint based Configuration
Configured Process
ILP SolverSWRL
Reasoner
PROCESS CONSTRAINTSQ: Cost <= $2000
Q: SupplyTime < 7 DaysL: Compat (S1, RAM, S2, MB)= True
L: preferredSupplier(S1) = TrueMin: Cost
SERVICE SETS IN INCREASING COST ORDER
1. R1, M2 Cost = $16002. R4, M3 Cost = $16203. R5, M1 Cost = $1700
COMPATIBLE SERVICE SETS IN
INCREASING COST ORDER
1. R1, M2 Cost = $16002. R5, M1 Cost = $1700
(REJECTED SET 2 as R4 not compatible wit M3)
CONSTRAINT ANALYZER
CANDIDATE SERVICES WITH CONSTRAINTS
RAM Candidate Service 1 (R1)Q: Cost = $800
Q: SupplyTime < 5 Days..
RAM Candidate Service N (RN)Q: Cost = $700
Q: SupplyTime < 8 Days
MB Candidate Service 1 (M1)Q: Cost = $850
Q: SupplyTime < 7 Days ...
MB Candidate Service M (MM)
Q: Cost = $950Q: SupplyTime <6 Days
UDDI
DISCOVERY ENGINE
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
Operation: Order
Pre: Ordered = False
Post: Ordered = True
Operation: Cancel
Pre: Ordered = True & Received = false
Post: Canceled=True & Ordered = false
Operation: Return
Pre: Ordered = True & Received = True
Post :Returned = True & Ordered = false and
Received = false
Event: Delayed
Pre: Ordered = True & Received = false
Post: Delayed=True & Ordered = True
Event: Received
Pre: Ordered = True & Received = false
Post: Received = True
Actions
Events
Flags
Ordered
Received
Delayed
Cancelled
Returned
Self Healing
Execution Graph- Generated from Operations, Events and Flags
5 Flags, thus 25 = 32 possible states (only 8 reachable states)
One proposed approach: Use Markov Decision Processes to
choose optimal actions
si1
si8
si2
si6
si5
si4
si7
si3
W
W
WW
Order
Return
Rec
Del
Rec
Cancel
Order
Cancel
Return
OrderOrder
0.45
0.35
0.85
S1- Ordered = True (All other flags false) S4 - Ordered = True and Received = falseS5-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
receive
orderMB
orderRAM
Wait for Delivery
Order
Return
Cancel
MBSupplier
WS
Order
Return
Cancel
RAMSupplier
WS
PM
SM1
SM2ILP
SolverCM SWRL
Reasoner
Listener 1: Monitor Current Exchange Rates
Listener 2: Monitor Supplier Discounts
Sympathethic Policy
Reconfigure process for immediate gain
May including canceling order from previous Supplier
Change in Currency Rate beyond threshold
Parasympathethic PolicyConsider 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., http://www.w3.org/Submission/WSDL-S/
[2] K. Verma, R. Akkiraju, R. Goodwin, Semantic matching of Web service policies, SDWP, 2005
[3] N. Oldham, K. Verma, A. Sheth, Semantic WS-Agreement Partner Selection http://lsdis.cs.uga.edu/projects/meteor-s/swaps/
[4] K. Sivashanmugam, J. Miller, A. Sheth, and K. Verma, Framework for Semantic Web Process Composition, IJEC, 2004
AWPs vs. Autonomic Computing
Autonomic Computing
Autonomic Web Processes
Databases Networks Servers
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