Autonomic Web Processes
Presenter: Amit Sheth
METEOR-S project, LSDIS Lab
Computer 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
Self
Self
Self
Configuring
Healing
Optimizing
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.
orderMB
receive
Wait for Delivery
orderRAM
Architecture
Autonomic Layer
Process
Manager
(PM)
Service
Manager
(SM)
Configuration
Manager
(CM)
Resources Layer
Process
Instances
Partner
Service
Configuration
Module (ILP,
SWRL)
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
CANDIDATE SERVICES
WITH CONSTRAINTS
DISCOVERY
ENGINE
UDDI
RAM Candidate
Service 1 (R1)
Q: Cost = $800
Q: SupplyTime < 5 Days
.
.
RAM Candidate
Service N (RN)
Q: Cost = $700
Q: SupplyTime < 8 Days
Constraint based
Configuration
Return
Cancel
MB
Supplier
WS (M2)
SM1
PM
orderMB
receive
CONSTRAINT
ANALYZER
SWRL
Reasoner
SERVICE SETS IN INCREASING
COST ORDER
1. R1, M2
Cost = $1600
2. R4, M3
Cost = $1620
3. R5, M1
Cost = $1700
Order
PROCESS CONSTRAINTS
Q: Cost <= $2000
Q: SupplyTime < 7 Days
L: Compat (S1, RAM, S2, MB)= True
L: preferredSupplier(S1) = True
Min: Cost
MB Candidate
Service 1 (M1)
Q: Cost = $850
Q: SupplyTime < 7 Days .
.
.
MB Candidate Service M
(MM)
Q: Cost = $950
Q: SupplyTime <6 Days
ILP Solver
Configured
Process
COMPATIBLE
SERVICE SETS IN
INCREASING COST
ORDER
1. R1, M2
Cost = $1600
2. R5, M1
Cost = $1700
(REJECTED SET 2
as R4 not compatible
wit M3)
Wait for Delivery
orderRAM
ILP
Solver
SM2
Order
Return
Cancel
RAM
Supplier
WS (R1)
CM
SWRL
Reasoner
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
Pre: Ordered = False
Events
Operation: Order
Post: Ordered = True
Pre: Ordered = True &
Received = false
Event: Delayed
Pre: Ordered = True &
Received = false
Ordered
Post: Delayed=True &
Ordered = True
Operation: Cancel
Post: Canceled=True &
Ordered = false
Flags
Received
Pre: Ordered = True &
Received = false
Delayed
Pre: Ordered = True &
Received = True
Operation: Return
Post :Returned = True &
Ordered = false and
Received = false
Event: Received
Post: Received = True
Cancelled
Returned
Self Healing
Execution Graph- Generated from Operations, Events and Flags
5 Flags, thus 25 = 32 possible states (only 8 reachable states)
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)
Order
Order
W
Order
si1
Cancel
si
Del
0.45
si
Cancel
si3
One proposed approach: Use
Markov Decision Processes to
choose optimal actions
Order
8
si
Return
Rec
si
0.35
5
W
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
Rec
2
0.85
6
W
si7
Return
si4
W
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
Order
Return
Cancel
MB
Supplier
WS
Change in Currency
Rate beyond threshold
Listener 1: Monitor
Current Exchange Rates
SM1
Sympathethic Policy
PM
Reconfigure process for immediate gain
orderMB
receive
Listener 2: Monitor
May including
canceling order from
Supplier Discounts
previous Supplier
Wait for Delivery
orderRAM
ILP
Solver
SM2
Order
Return
Cancel
RAM
Supplier
WS
CM
SWRL
Reasoner
Parasympathethic Policy
Consider long term supplier
relationship
Model
•
Functional and Data Semantics
•
Non-Functional Semantics
– Service (WSDL-S)[1]
– 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]
AWP
Property/
Type of
Semantics
Self
Configuring
Self
Healing
Data
Functional
NonFunctional
Execution
Ontologies
– Domain Specific Ontologies,
– Domain Independent/Upper Ontologies
[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
Self
Optimizing
AWPs vs. Autonomic Computing
Autonomic Web Processes
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.
Autonomic Computing
Autonomic IT
Infrastructure
Databases
•Self Configuring: Lower IT
cost on maintenance and
deployment.
•Self Healing: Lower human
involvement in problem
detection, analysis and solving.
Networks
Servers
•Self Optimizing: Better SLAs
to customers of the IT
infrastructure.
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, multiagents, 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