Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


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. 2007 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 100 95 80...... 65 45 30 15 100 95 80 79 70 65 45 30 15 1001.0 65 30 100 650.7 300.25 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 (alrifai@L3S.de) Thomas Risse (risse@L3S.de)


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

Similar presentations


Ads by Google