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Motivation - Desktop studies - Desktop systems - Enhancing TEL - Outlook Combining Global Optimization with Local Selection for Efficient QoS-aware Service.

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Presentation on theme: "Motivation - Desktop studies - Desktop systems - Enhancing TEL - Outlook Combining Global Optimization with Local Selection for Efficient QoS-aware Service."— Presentation transcript:

1 Motivation - Desktop studies - Desktop systems - Enhancing TEL - Outlook Combining Global Optimization with Local Selection for Efficient QoS-aware Service Composition Mohammad Alrifai and Thomas Risse The International WWW Conference – April 22 th, 2009 L3S Research Center University of Hanover Germany

2 Introduction Broker QoS Registry Service provider Service Consumer Find service QoS Feedback Update QoS UDDI Publish Service invocation QoS-aware Architecture* Service provider Service Consumer Find service UDDI Publish Service invocation Web service Architecture * Liu et al: QoS Computation and Policing in Dynamic Web Service Selection – in WWW 2004

3 INPUT: Abstract Process task web service WWW Discovery Abstract representation: workflow-like languages: e.g. BPEL Web service discovery: Matching functional requirements: e.g. credit card verification, flight booking, etc. Web service selection: Fulfilling Non-functional requirements: e.g. latency, availability, price, etc. Dynamic Web Service Composition OUTPUT: Executable Web Process QoS-based Selection Alternative web services

4 Outline Introduction QoS computation model Global vs. Local QoS Optimization A hybrid approach Experimental evaluation Conclusion and future work

5 QoS Computation Model QoS Attributes (q): o Quantitative: e.g. price ($), availability (uptime%), response time (sec) o Positive (e.g. availability) o Negative (e.g. price) QoS vector (Q): o Component service: Q s = {q 1, q 2,..., q r } o Composite service: Q cs = {q 1, q 2,..., q r } where q is the aggregated QoS value QoS constraints vector(C): o Local constraints: C s = {c 1,..., c r } upper bound values for Q s o Global constraints: C cs = {c 1,..., c r } end-to-end upper bound values for Q cs

6 Problem statement: Given a composition request CS = {S 1, S 2,..., S n }, a list of service candidates for each service class S j in CS, a vector of m end-to-end QoS constraints C cs = {c 1, c 2,..., c m }, and a utility function, select one web service s j for each service class S j in CS such that: (1) q k c k, 1 k m, i.e. all constraints are satisfied ( 2) Overall utility is maximized QoS Optimization Problem Feasible Solutions: Any selection that fulfills (1) Optimal Solution: Any selection that fulfills (1) and (2)

7 Local QoS Optimization*: o Component services are selected independently o Service candidates are ranked by utility value o Very efficient (linear complexity) o Distributed computation o Cannot satisfy end-to-end QoS constraints Abstract services Alternative services Existing Solutions I * Liu et al: QoS Computation and Policing in Dynamic Web Service Selection – in WWW 2004 Concrete services

8 Global QoS Optimization*: o The problem is modeled as a Mixed Integer Linear Program Existing Solutions II * Zeng et al: Quality Driven Web Services Composition – in WWW 2003 * Ardagna et al: Adaptive Service Composition in Flexible Processes - in IEEE Trans. on Software Eng Service composer Service Broker 1 Service Broker 2 Service Broker n Candidate Services Candidate Services Candidate Services QoS Registry OUTPUT: Executable composite service INPUT: Abstract composite service

9 Global QoS Optimization*: o Existing MILP solvers can be used to find the optimal solution o Can satisfy end-to-end QoS constraints o Inefficient: exponential complexity w.r.t. number of services o Supports only linear utility functions o Centralized computation o Re-computation is required in case of service failure Existing Solutions II * Zeng et al: Quality Driven Web Services Composition – in WWW 2003 * Ardagna et al: Adaptive Service Composition in Flexible Processes - in IEEE Trans. on Software Eng. 2007

10 Our goal: a compromise between performance and optimality Divide the problem into two sub-problems that can be solved more efficiently than the original problem A Hybrid Approach Constraint Decomposition Global QoS constraints Local QoS constraints Local Selection QoS Registry Step1 (Global optimization): each global QoS constraint is decomposed into a set of local constraints Step2 (Local Optimization): the best service candidate that satisfies local constraints is selected Local Selection

11 A non-trivial task o Different service classes can have different distributions of QoS values Proposed approach: Decomposition of QoS Constraints I o Map global constraints into local quality levels, such that: Selected quality levels serve as conservative local constraints Local constraints are relaxed as much as possible o Extract quality levels for each class based on local characteristics

12 1.Extracting Quality levels of service class S j : a), divide the QoS value range into d sub-ranges b)Randomly select one value q k z from each sub-range, 1 z d c)Assign each level q k z a value p k z between 0 and 1, which estimates the benefit of using this level as local constraint: Decomposition of QoS Constraints II Quality Levels qkzqkz pkzpkz

13 2.Mapping global QoS constraints into local quality levels: o using Mixed Integer Linear Programming o Objective function: Decomposition of QoS Constraints III o A binary variable x jk z for each quality level q jk z : o Objective function: o Constraints:

14 o Local constraints are sent to service brokers to perform local selection Local Selection I Service composer Service Broker 1 Service Broker 2 Service Broker n Quality levels Local constraints Quality levels Local constraints Quality levels QoS Registry INPUT: Abstract composite service Step 1: Decomposition of global QoS constraints Service composer Service Broker 1 Service Broker 2 Service Broker n Best local candidate 1 Best local candidate n Best local candidate 2 QoS Registry OUTPUT: Executable composite service Step 2: Local selection of best candidates

15 o Filtering: Service brokers filter out services that violate local constraints o Ranking (Simple Additive Weighting method): o Normalization: relative distance to worse value o Weighting: represents user priorities w k = weight(q k ), 0 w k 1, w k =1 Local Selection II

16 Service candidates QoS attributes Utility values normalization weighting Service Candidates sum Local Selection III

17 Evaluation methodology 1.Two datasets: Real dataset (QWS*) and synthetic dataset (normally distributed) 2.Random assignment of services to classes 3.Given a set of global QoS constraints select the best component services using: a.Global optimization approach (Mixed-Integer Linear Programming) b.Our hybrid approach 4.Measure the performance of both approaches (computation time) 5.Measure the distance to optimal results: optimality (%) = utility of obtained solution / utility of optimal solution Experimental Evaluation * Al-Masri et al: Investigating web services on the world wide web-in WWW2008

18 Results I

19 Results II

20 We have proposed a scalable service selection method that is able to achieve close-to-optimal results with low cost and can be implemented in a distributed infrastructure. The idea: divide the problem into two sub-problems: 1.Constraint decomposition: solved by global optimization 2.QoS optimization: solved by guided local selection Next steps: o Developing adaptive methods for determining quality levels o Scalability with respect to num. of service classes Conclusion and Future Work

21 Thank you! Mohammad Alrifai Thomas Risse


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