CSSSIA Workshop – WWW 2008 Speeding up Web Service Composition with Volatile External Information John 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 Deltas 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 Start Delta 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 a max at every step (state) without look ahead Start ……… Finish a 2 (s 0 ) a 1 (s 0 ) a n (s 0 ) a 2 (s 1 ) a 1 (s 1 ) a n (s 1 ) a 2 (s m ) a 1 (s m ) a n (s m ) a max Start a 2 (s 0 ) Start a 2 (s 0 ) Start

Greedy Composition Problem formalization 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: Importance of CostImportance of Availability Normalized Cost Normalized Availability 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 Service start Ticket Expires Query issuedQuery response received Backtrack if needed end Hotel Service HALTS

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 Service start 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 Service start Ticket Expires Query issued CONTINUE Query response received Backtrack if needed end Hotel 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. Theorem Typically, there are more parameter vectors for which the above is true!

Informed-Presumptive Gradient 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 Value using optimal policy Value using current policy

Informed-Presumptive Gradient Descent –Computationally inexpensive to find the region Given that V a is a linear function of the parameters of a service a, the gradient : is constant. Theorem

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