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

2. Web service Architecture
Introduction
Web service Architecture
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
* Liu et al: QoS Computation and Policing in Dynamic Web Service Selection – in WWW 2004

3. INPUT: Abstract Process Alternative web services
Dynamic Web Service Composition
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.
INPUT: Abstract Process
WWW
Discovery
Alternative web services
OUTPUT: Executable Web Process
QoS-based Selection
task
web service

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

5. QoS Computation Model QoS Attributes (q): QoS vector (Q):
Quantitative: e.g. price ($), availability (uptime%), response time (sec)
Positive (e.g. availability)
Negative (e.g. price)
QoS vector (Q):
Component service: Qs = {q1, q2, ..., qr}
Composite service: Qcs = {q‘1, q‘2, ..., q‘r} where q‘ is the aggregated QoS value
QoS constraints vector(C):
Local constraints: Cs = {c1, ..., cr} upper bound values for Qs
Global constraints: Ccs = {c‘1, ..., c‘r} end-to-end upper bound values for Qcs

6. QoS Optimization Problem
Problem statement:
Given a composition request CS = {S1, S2, ..., Sn},
a list of service candidates for each service class Sj in CS,
a vector of m end-to-end QoS constraints Ccs = {c‘1, c‘2, ..., c‘m},
and a utility function,
select one web service sj for each service class Sj in CS such that:
(1) q‘k ≤ c‘k , 1 ≤ k ≤ m, i.e. all constraints are satisfied
(2) Overall utility is maximized
Feasible Solutions:
Any selection that fulfills (1)
Optimal Solution:
Any selection that fulfills (1) and (2)

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

8. Existing Solutions II Global QoS Optimization*:
The problem is modeled as a Mixed Integer Linear Program
OUTPUT: Executable composite service
INPUT: Abstract composite service
Service composer
Candidate
Services
Candidate Services
Candidate
Services
Service Broker 1
Service Broker 2
Service Broker n
QoS Registry
QoS Registry
QoS Registry
* 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

9. Existing Solutions II Global QoS Optimization*:
Existing MILP solvers can be used to find the optimal solution
Can satisfy end-to-end QoS constraints
Inefficient: exponential complexity w.r.t. number of services
Supports only linear utility functions
Centralized computation
Re-computation is required in case of service failure
* 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. Constraint Decomposition
A Hybrid Approach
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
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
Local Selection
Local Selection
Step2 (Local Optimization):
the best service candidate that satisfies local constraints is selected

11. Decomposition of QoS Constraints I
A non-trivial task
Different service classes can have different distributions of QoS values
Proposed approach:
Extract quality levels for each class based on local characteristics
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

12. Decomposition of QoS Constraints II
Extracting Quality levels of service class Sj:
, divide the QoS value range into d sub-ranges
Randomly select one value qkz from each sub-range, 1 ≤ z ≤ d
Assign each level qkz a value pkz between 0 and 1, which estimates the benefit of using this level as local constraint:
100 95 80
100 95 80 . 65 45 30 15
qkz
pkz
100
100
1.0 79 70 65 65 65
0.7 30 30
0.25 45 30 15
Quality Levels

13. Decomposition of QoS Constraints III
Mapping global QoS constraints into local quality levels:
using Mixed Integer Linear Programming
Objective function:
A binary variable xjkz for each quality level qjkz:
Objective function:
Constraints:

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

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

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

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

18. Results I

19. Results II

20. Conclusion and Future Work
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:
Constraint decomposition: solved by global optimization
QoS optimization: solved by guided local selection
Next steps:
Developing adaptive methods for determining quality levels
Scalability with respect to num. of service classes

21. Thank you! Mohammad Alrifai (alrifai@L3S.de)
Thomas Risse