A Method for Runtime Service Selection Hong Qing Yu Internal seminar (18/10/2007) Department of Computer Science

Outline  Web service selection problems  Competable definition  Runtime service selection  Multicriteria aggregation methods  LSP method  OWA operators  A modified LSP method for service selection  Future work plan

Service selection problems based on mulitcriteria  When are the services competable?  What is the most significant difference between design time service selection and run time selection?  How can we measure the individual criterion of each service?  How can we aggregate the muilticriteria to get final evaluation result for each comparable service?

Run time competable definition Definition 1: Services are comparable in run time, iff their input, output, precondition, effect (IOPE) are comparable. Input comparable: I service I requirement Output comparable: O service O requirement Precondition comparable: P service = P requirement Effect comparable: E service = E requirement

Run time competable definition  Example Input = {departure time, return time, name} Output = {Reference number} Precondition = {Registered, login} Postcondition = {Ticket is blocked} Input = {departure time, return time} Output = {Reference number, price} Precondition = {Registered, login} Postcondition = {Ticket is blocked} Input = {departure time, return time} Output = {Reference number} Precondition = {Registered, login} Postcondition = {Ticket is blocked}

Runtime service selection  Runtime service selection: Automatic selecting a best suitable service based on desired Non-functional criteria for dynamically service composition. (If there are comparable services)  For example Register Flight Booking Hotel Booking Payment

Runtime service selection  Non-functional properties/QoS includes: [IBM] Availability Accessibility Integrity Performance Reliability Regulatory Security Cost [More] Time consuming Location Language Devices supporting …

MultiCriteria aggregation methods  Arithmetric aggregation  Geometric aggregation

MultiCriteria aggregation methods  LSP aggregation

LSP method - orness  Orness degree (d) depends on what kind of aggregation function M(x)  0.5<d<1 : replaceability  0<d<0.5 : simultaneity  0<d<1/3 : mandatory

LSP method - example Integrity Cost Reputation Without r With r

OWA: a fuzzy set operator  Definition: An OWA operator of dimension n is a mapping F : R n -> R, that has an associated n vector W = (w 1, w 2, …w n ) T such as w i [0, 1]; 1 i n, and W = (w 1 +w 2 +…+w n = 1).  F(a 1, a 2, … a n ) = w 1 b 1 +w 2 b 2 +…+w n b n  b j is the j-th largest element of the bag.

OWA - example  For example, assume W = [0.4, 0.3, 0.2, 0.1], F(0.7,1, 0.3, 0.6) = (0.4)(1)+(0.3)(0.7)+(0.2)(0.6)+(0.1)(0.3)=0.76.  A fundamental aspect of this operator is the re- ordering step, an aggregate a i is not associated with a particular weight w i but rather a weight is associated with a particular ordered position of aggregate

OWA - orness  This orness measurement function can be proved equal to Fodor’s orness measurement function, when OWA operator is applied.

Combining OWA operator with LSP Integrity Cost Reputation (0.6, 0.5, 0.2) (0.1, 0.7, 0.2)w (0.1*2+0.7)/2=0.45 (0.9, 03, 0) (0.7, 0.1, 0.2)w (0.7*2+0.1)/2=0.75 Orness (d)=( )/2 = r ≈ 2.0 (0.6, 0.5, 0.2)(0.1, 0.4, 0.5)w (0.1*2+0.4)/2=0.3 (0.9, 03, 0)(0.4, 0.1, 0.5)w (0.4*2+0.1)/2=0.45 Orness (d)=( )/2 = r ≈

Dujmovic’s LSP method  Dujmovic’s LSP method includes five major steps: 1.Specifying evaluation criteria (manually) 2.Defining evaluation methods for each criterion (manually) 3.Orness degree analysis (manually) 4.Local aggregation and global aggregation (manually) 5.Cost/benefit analysis (manually)

A modified LSP method for service selection  Our proposed the modified LSP method for service selection has four major steps: 1.Specifying evaluation criteria for a group services which are in the same services category (manually) 2.A unified type-based evaluation methods are defined for all kinds of criteria (automatically) 3.OWA combining degree analysis/decision (automatically) 4.Aggregating soft criteria and hard criteria to get final result (automatically/statically)

A modified LSP method for service selection 1.Service selection concept model 0<W<1, bigger evaluation value is desired (soft) -1<W<0, smaller evaluation value is desired (soft) W=1, the criterion is hard requirement

Relevance engine 2. Type-based evaluation methods (1) Value metric (2) Boolean metric (3) Set metric

A modified LSP method for service selection  Automatic orness analysis and calculation: W = (w 1, w 2, … w n ) F 1 = (a 11, a 21, … a 1n )->W 1 ’->d1 F 2 = (a 21, a 22, … a 2n )->W 2 ’->d2 … F n = (a n1, a n2, … a nn )->W n ’->dn Orness(d)=

A modified LSP method for service selection  Aggregation 0 >0 0 0

Conclusion  Web service selection problems  Competable definition  Runtime service selection  Multicriteria aggregation methods  LSP method  OWA operators  A modified LSP method for service selection

Future work plan  Complexity analysis  Implementation and evaluation

Questions