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