CSSSIA Workshop – WWW 2008

Speeding up Web Service Composition with Volatile

External InformationJohn Harney, Prashant Doshi

LSDIS Lab, Dept. of Computer Science,University of Georgia

Presented By:Haibo Zhao

LSDIS Lab, Dept. of Computer Science,University of Georgia

Adaptation of WS Compositions• Many Web service composition (WSC) techniques

assume static environments– Service parameters are assumed fixed

• Environments are often dynamic – Examples -- Trip planner

• Price of an airline ticket offered by a service (such as Delta’s online booking) increases

• Availabilities of rooms at a specific hotel may increase or decrease

• Service response time increases due to network outage

Optimal WSC• Optimality – Selecting services so as to minimize

cost of trip and optimize other parameters

• Underlying objective – maintain WSC optimality• Depends on how accurately the services’ parameters are

captured• Requires cognizance of changes in parameter values that

may have occurred• Backtracking to previous points in the composition and

recomposition may be needed

Motivating Scenario – Travel Planning

StartDelta

Ticketing Service

Rental car Service

Hotel Service

Ticket price expires

Backtrack prior to selection of Airline

Ticket Service

United Ticketing Service

Finish

Delta ticket price increases

United ticket becomes cheapest available

Greedy Composition

• Composition method selects the best service amax at every step (state) without look ahead

Start

… … …

Finisha2(s0)

a1(s0)

an(s0)

a2(s1)

a1(s1)

an(s1)

a2(sm)

a1(sm)

an(sm)

amax

amax

amax

Starta2(s0)

Starta2(s0)

StartStartStart

Greedy Composition

Problem formalization

},,,ˆ,,,{ 0 AvCsAAsSP g

Set of all states

Set of all candidate WSs

Initial State

Availability of services

Set of possible WSs that can be invoked at a state

Cost of services

Goal state

Greedy Composition

• A service a is chosen based on its value:a

Av

a

Ca AvwCwV

Importance of Cost Importance of Availability

Normalized Cost Normalized Availability

a

sAa

Vs)(ˆ

maxarg)(*

Service selected is the one that yields the greatest value.

Policy for choosing the best service at each state

Reactive Query Policies

• Introduced by Au and Nau (2006)– Reactive Query Policy

• Set of rules that decide when expired parameters of services should be queried and revised values integrated during the composition

– Three policies were introduced• Eager• Lazy• Presumptive

Reactive Query Policies

• Eager – When a service expires, query that service and halt

composition until answer arrives; if needed, backtrack and recompose

Airline Ticket

Service

Rental Car

Servicestart

Ticket Expires

Query issued Query response received

Backtrack if needed

endHotel

ServiceHALTS

Reactive Query Policies

• Lazy – When a service expires, query that service and continue

composition until goal is reached; if needed, backtrack and recompose

Airline Ticket

Service

Rental Car

Servicestart

Ticket Expires

Query issued

CONTINUE

Query response received

Hotel Service

Backtrack if needed

end

Reactive Query Policies

• Presumptive – When a service expires, query that service and continue

composition until the answer to the query is received; if needed, backtrack and recompose

Airline Ticket

Service

Rental Car

Servicestart

Ticket Expires

Query issued

CONTINUE

Query response received

Backtrack if needed

endHotel

Service

Reactive Query Policies

• All previous policies suffer from:– Excessive backtracking (some backtracking is

not required)– Checks if the value of the service parameter

changes rather than if the policy changes

Not all changes in the values of the parameters cause changes in the composition!

Our Approach: Informed-Presumptive• Improves the presumptive approach

– Identifies regions of revised parameter values for which the optimal policy does not change

– Uses gradient descent to identify the regions

For a WSC, there exists at least one revised parameter vector

for which the value of using the current policy with is the same

as the value of using the optimal policy with .

},{'' aaa AvCp

Theorem

Typically, there are more parameter vectors for which the above is true!

ap

ap

Informed-PresumptiveGradient Descent

– How is the descent performed?

Value Difference

Cost

Availability

Start with 2 distinct points on the surface

Use gradient to descend down surface

Descend until Value Difference is zero

Form a line

Plot of Cost vs Availability vs Value Difference

Informed-Presumptive

• Presumptive vs Informed-Presumptive

Value Difference

Cost

Availability

Presumptive does not backtrack if the revised values are unchanged

Informed-presumptive does not backtrack if the revised values fall in region that policy is unchanged

VV *

VV **V

V

Value using optimal policy

Value using current policy

Informed-Presumptive

• Gradient Descent– Computationally inexpensive to find the region

Given that Va is a linear function of the parameters of a service a, the gradient :

is constant.

Theorem

})(

,)(

{)( a

a

a

aa

Av

pE

C

pEpE

Empirical Results• Experimental Setup

– Change Likelihood (probability that upon expiration, a service parameter will change)– Compared Informed-Presumptive against the previous existing strategies (Lazy, Eager, Presumptive) for the following:

• Time required to complete the WSC• Number of backtracks in the composition• Number of compositions that were not completed

Empirical Results

• Average Composition Times

As likelihood of change increases, the informed-presumptive out performs others by a wide margin

For lower change likelihoods, presumptive may outperform informed-presumptive

Reason: overstepping in gradient descent leads to slight error in division line

Empirical Results

• Average Number of Backtracks

As likelihood of change increases, the informed-presumptive backtracks less than the others

Note: Lazy approach performs limited backtracking, but the backtracks are more costly than the informed presumptive

Empirical Results

• Average Number of Incomplete Compositions

As likelihood of change increases, the informed-presumptive has less “incomplete” compositions

Conclusion

• Many Web service compositions must function in dynamic environments– Backtracking may be required

• Previous composition strategies perform redundant backtracks

• Our Informed-Presumptive strategy:– eliminates unnecessary backtracks – implemented without much extra overhead– outperforms previous strategies

Future Work

• Compositions with nonlinear combinations of parameters

• Challenges:– Dependencies between services– Gradient descent could be more expensive

computationally

Thank you

Questions