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India Research Lab

© 2005 IBM Corporation

IBM Research

Towards End to End Composition of Web Services

Biplav Srivastava*IBM India Research Lab, New Delhi

at TIFR on Jan 21, 2005*Joint work with Neeran Karnik, Vikas Agarwal, Arun Kumar, Ashish Kundu, Koustuv Dasgupta and Sumit Mittal

India Research Lab

January 21, 2005 © 2005 IBM Corporation

IBM Research

Outline

Why do compositionBusiness-inspired case

Understand potential and scope: end-to-end

Highlight issues and non-issues

A Solution Approach Decouple into ‘what’ needs to be composed (logical composition) and ‘how’ (physical composition)

A walkthrough of prototype

Practical ChallengesInformation Modeling

Measuring impact

India Research Lab


IBM Research

Customer Problem (Mobile Telephony Service Providers)

Voice service is commoditizedHigh volume but low margins

Mobile user services are the differentiator

Data services (e.g. stock quotes, banking)

Gateways to “brick-n-mortar” services (e.g. movie tickets)

Location-aware services

Need rapid service / application development and deployment

e.g., in response to competitor, or customer demand

State of the art: manual programming, integration too slow, long time-to-market huge implications on break-even point, revenue

Source: “The Value of the IBM WebSphere Application Server for Telecom”, Scott Broussard (IBM Austin), March 11, 2003

India Research Lab


IBM Research

Creating a new service…

Specify end-user service capability

Select vendor service providers

Design theflow

Deploy theservice

Fig.1: Creating a new end-user service

New service

capabilities

New service

providers

Network / environment

changes

• Manual business process integration

• Use tools like WSAD-IE to create flows and business logic

• Deploy using a flow engine (such as MQWF / WAS Flow Orchestrator)

• Must take changes into account

| |

India Research Laboratory


Customer Problem…

Service/content providers are often 3rd parties Telco is the intermediary for delivery of services to enterprises/consumers

Must improve ease-of-use of its software infrastructure Must optimize the utilization of its IT infrastructure

scalability under high-volume usage

Need to adopt standards-based frameworkUse Web services to build end-user services

component-based software engineering, software reuseUse semantic annotations allowing service functionality to be programmatically composedUse autonomic, decentralized orchestration of services

3rd PartyProviders

Telco Enterprise

User

User

User

| |



Helpline Automation Scenario

Service Specifications Input – Customer Problem Output – Status of Problem Resolution

Steps in the workflow Reporting of the problem using a Web-

based interface Selection of a helpline agent to resolve

the problem Agent should have desired level of

expertise Agent could be a desk-based expert, or a

field-based expert Notify the appropriate expert with

relevant information about the problem Update registry with resolution status

Goal is to create the service using existing component services, in an automated manner if feasible

Problem Ticket ID, Field Agent ID

Problem Ticket ID, Resolution Status

Customer ProblemProblem Reporting

Service

Agent AssignmentService

Help DeskService

On SiteService

Problem RegistryService

Problem Ticket ID

Problem Ticket ID,Desk Agent ID

Problem Ticket ID, Resolution Status

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Component Services

Telco services Location tracker, SMS, Email, Pager, Instant Messaging, Call

Setup, Distance calculator

Domain-specific Services Problem Reporting Service

Collects relevant information about a problem via customer interaction Problem Classification Service

Classifies based on level of expertise and the desired location of expert Optimal Agent Selector Service

Finds an expert on the field closest to a customer site Database Services

Customer DB, Product DB, Expert DB, Problem Registry DB

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Composed Service: Location-Based Agent Selection Service

Location-basedSelection Service

Effect: Agent chosen based on proximity to destinationOutput:Field Agent ID

Input:Customer Location,List of Field Agent IDs

Expert DBLocation Tracker

DistanceCalculator

Optimal AgentSelectorList of Agent IDs

List of Agent Mobile Nos

List of Agent Locations

List of Agent Distances,List of Agent IDs

Nearest Agent IDCustomer Location

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Understanding the Business Problem

Major impact in application integration/ management of IT assets possible ($$) Scope is end-to-end composition

Offline service creation, deployment User in the loop, ratification important Online monitoring for Quality of Service,

automatic re-provisioning Could drive composite service life cycle

Issue Scalability of composition solution Automate service creation – control flow and data flow Modeling consistent with software engineering principles

Non-issue Fully automatic service creation because people want to be in the loop when costly

and business-critical assets are involved Usage of any specific ontologies, semantic web, etc. Metadata needs to be

represented formally and be consistent with existing modeling methodologies.

Functionality (what)

Execution(how)

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Proposed Solution

On Demand Business Process Integration for mobile SPs

Automatic Service Composition Represent business processes as web services Enhance service descriptions using semantic annotations Express requirements/goals using semantic representation Programmatically compose web service functionality to achieve desired

goal

Autonomic Service Deployment and Orchestration Decentralize the composite service based on static program analysis Deploy on a distributed infrastructure Monitor its execution at runtime Optimize the deployment periodically

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System Architecture

Abstract Workflow

(Plan)

Domain Ontolog

y

Service Registry

DeployableWorkflow

ServiceSpecificatio

n

Logical Composer

Physical Composer

Execution Environment

Service CreationEnvironment

Key Components Service Capabilities Database

Information about services available in-house as well as with 3rd party providers

Telecom Ontology Domain-specific terminology

Logical Composer including Planner automated aggregation of services via

generative planning-based reasoning techniques

Physical composer Instance selection based on end to

end QoS specification

Input Requirements document for the new

service that needs to be composed

Output Deployable workflow representing a

composite service

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Logical Composition

ValidatorOntology

SNOBASE

MatchmakerFilterPlanner4J

ServiceSpecs

Abstract Workflow

ServiceCapabilities

Registry

Logical Composer Available

ServiceTypes

DomainInformation

CandidateComponent

Services

DomainInformation

CandidateComponent

Services

ValidatedSpecs

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Physical Composition

Instance Selector

BPEL Generator

AbstractWorkflow

DeployableWorkflow

Data Dictionary

WSME

Matchmaker

ServiceInstancesRegistry

PhysicalComposer

Available ServiceInstancesSelected

Instances

WSDLs for the selected Instances

Matchmaking language/rules

MatchingInstances

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Demo Walkthrough

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Composed Service: Helpline Automation

Problem Reporting

RegistryUpdate

CallSetup

Help Desk

MessageDelivery

On Site

Problem Classification

Location-basedAgent Selection

Expert LookupAgent Assignment

Desk-based Expert ID Field Expert ID

Problem Ticket

Problem Ticket, Problem Ticket,

Resolution StatusProblem Ticket,

Resolution StatusProblem Ticket,

Customer Interaction

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Practical challenges

End to end information modeling Planning gives control flow but not data flow readily. Data flow generation

needs contextual information about inputs and outputs. Possible to generate data flow but it will bloat the service specifications Will cause frequent specification changes to information model

Handling non-functional requirements, QoS constraints Scalability – OWL-S supports at web service instances level

Measuring impact How to quantify gain with a composition tool v/s manual methodology How on-demand can the system be?

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Concluding comments

Web services composition important to solve real business problems Presented application integration for a telecom provider

Need end-to-end, continual composition perspective Existing distributed computing solutions need to leveraged – CIM,

Monitoring, Execution Infrastructure, Grid, Autonomic Computing, etc.

Presented an approach and prototype tool Decouples composition into what and how phases Uses AI and distributed computing techniques collectively

Presented practical challenges

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