A user-driven policy selection model

Transcription

1 A user-driven policy selection model Mariagrazia Fugini, Pierluigi Plebani, Filippo Ramoni Dipartimento di Elettronica ed Informazione Politecnico di Milano

2 Motivation 2 Web service description should consider functional aspects (what the Web service does) quality aspects (how well the Web service works) About the functional aspects some standards or commonly accepted languages are available: e.g., WSDL, WS-BPEL,... About the non-functional aspects there are only some attempts: e.g., WSOL, WS-Policy framework,... This lack also reflects on the Web service discovery activity

3 Goals 3 A quality of service model able to describe quality aspects of a Web service express user requirements and service capabilities consider user preferences A policy selection model exploiting such quality model relying on the WS-Policy framework Issues: Providers, tech-savvy users, and simple end-users might adopt different terminologies: technical vs. high-level Analytic Hierarchy Process (AHP) is adopted as a technique to map user preferences

4 Quality of service model elements 4 Quality dimension model how to define a single quality dimension (a.k.a. aspect, attribute, parameter) how to map dependencies among quality dimensions Quality definition model how to organize quality dimensions in a global description Quality evaluation model how to evaluate the quality of a service

5 Quality dimension model 5 Two kinds of quality dimensions: primitive quality dimensions (pqd): can be directly measured derived quality dimensions (dqd): depends on other dimensions pqd = <name, values> name: dimension identifier values: continue or discrete range of admissible values dqd = <name, f(pqdi, dqdj)> name: dimension identifier f(pqd i, dqdj): dependency function among dqd and subdimensions

6 Examples 6 Primitive quality dimensions <resolution, {320x200, 800x600, 1024x768}> <framerate, [1..30]> <price, [0.. )> Derived quality dimensions <videoquality, f(framerate, resolution, colordepth)> <security, f(integrity, confidentiality, authenticity, non-repudiation)>

7 Quality definition model 7 Class of users Quality Tree Domain application community produce read Quality Tree read User Quality Tree User produce User policy Provider Service policy produce

8 Community 8 Is Bandwidth a primitive or derived quality attribute? We can measure the bandwidth We can also calculate the bandwidth we need The community is in charge of: collecting all the relevant quality dimensions organizing them according to a Quality Tree

9 Quality Tree 9 Quality of service Video quality Sound quality Resolution Framerate Colordepth Price Encoding Sampling { 320*200; 800*600; [10..30] { 8; 16; 24; 32 } [0.. ) { MP3; WMA; ALAC } [ ]

10 Actors involved 10 Class of users Quality Tree Domain application community produce read Quality Tree read User Quality Tree User produce User policy Provider Service policy produce

11 Service capabilities 11 Resolution Framerate Colordepth { 320*200; 800*600; [10..30] Video { 8; 16; quality 24; 32 } A1 { 800*600; [10..30] { 16; 24} A2 { 800*600; Quality of service [20..30] { 16; 24; 32} Service Policy <policy> <exactlyone> A1 A2 </exactlyone> </policy> Sound quality Price Resolution Framerate Colordepth Price Encoding Sampling { Encoding 320*200; 800*600; [0.. ] [0..3] { MP3; [10..30] { 8; { 16; MP3; WMA; ALAC } 24; WMA 32 } } [0..1] [0.. ] { MP3; WMA } { MP3; WMA; ALAC } [ ] Sampling [ ] [ ] [ ]

12 Actors involved 12 Class of users Quality Tree Domain application community produce read Quality Tree read User Quality Tree User produce User policy Provider Service policy produce

13 Weighted Quality Tree 13 Quality of service Video quality Sound quality Resolution Framerate Colordepth Price Encoding Sampling { 320*200; 800*600; [10..30] { 8; 16; 24; 32 } [0.. ) { MP3; WMA; ALAC } [ ]

14 Analytic Hierarchy Process (AHP) 14 Decision making technique developed by L. Saaty (1980) Suitable for hierarchical structure as the quality tree is Given a dimension we assign to each sub-dimension a score We fill the comparison matrix The score represents the overall performance with respect to the different parameters

15 Analytic Hierarchy Process (AHP) 15 Res FR CD ai,j Definition Res 1 1/7 3 FR CD 1/3 1/9 1 1 Equal importance 3 Moderate importance 5 Strong importance 7 Demonstrate importance 9 Extreme importance 2,4,6,8 Intermediate values Eigenvector of comparison matrix represents the performance of all the parameters <0.150; 0.785; 0.065>

16 Evaluation function 16 Continue range e framerate Discrete range CD / /7 1/ /9 1/5 1/3 1 framerate

17 User quality tree 17 High = res >= 1024x768; fr>20;cd>8 Low = res<1024x768; fr >= 20;cd <= 8 Quality of service High: ENC = ALAC, SAM > 128 Medium: ENC = WMA, 64 <= SAM <= 128 Low: ENC = MP3, SAM < 64 Video quality Sound quality Resolution Framerate Colordepth Price Encoding Sampling x xxx e framerate e price 1 x xxx x xxx x xxx x xxx x xxx x xxx x xxx x xxx x xxx 0 30 framerate price x xxx x xxx

18 Actors involved 18 Class of users Quality Tree Domain application community produce read Quality Tree read User Quality Tree User produce User policy Provider Service policy produce

19 User requirements 19 Resolution Framerate { 320*200; 800*600; [10..30] U1 { 800*600; [20..25] User Policy <policy> <exactlyone> U1 </exactlyone> </policy> Colordepth* { 8; 16; 24; 32 } { 16; 24} Price [0.. ] [1..3] Sound Quality { high; medium; low } {medium}

20 Selection process 20 Composed by two main steps satisfiability evaluation alternative ranking The first step realizes if a service policy alternative is able to satisfy all the user requirements primitive quality dimensions are compared (matchmaking) The second step ranks the satisfiable service policy alternatives quality evaluation function is considered (selection)

21 Satisfiability evaluation 1/2 21 U1 U1 A1 Resolution { 800*600; Resolution { 800*600; { 800*600; Framerate [20..25] Framerate [20..25] [10..30] Colordepth* { 16; 24}* Colordepth { 16; 24}* { 16; 24} Price [1..3] Price [1..3] [0..3] Sound Quality {medium} Encoding { WMA } { MP3; WMA } Sampling [ ] [ ]

22 Satisfiability evaluation 2/2 22 U1 U1 A2 Resolution { 800*600; Resolution { 800*600; { 800*600; Framerate [20..25] Framerate [20..25] [20..30] Colordepth* { 16; 24}* Colordepth { 16; 24}* { 16; 24; 32} Price [1..3] Price [1..3] [0..2] Sound Quality {medium} Encoding { WMA } { MP3; WMA } Sampling [ ] [ ]

23 Alternative Ranking Quality of service Video quality Sound quality Resolution Framerate Colordepth Price Encoding Sampling { 800*600; [10..30] { 16; 24} [0..3] { MP3; WMA } [ ]

24 Concluding remarks 24 We propose a quality of service model which considers the dependency among quality dimensions the user preferences User requirements and service capabilities are described according to policies The user preferences are defined according to AHP The quality model drives the policy selection model

25 Future work 25 The price should be considered as a special quality dimension Users and providers better at evaluating quality of service according to a cost-benefit analysis Ranking activity can be followed by a negotiation process Generation of user quality tree should be simplified and possibly fully automatized Integrating semantic analysis as a way for supporting the community Application based on the proposed approach is now available (

26 26 Thanks for your attention! Mariagrazia Fugini Pierluigi Plebani Dipartimento di Elettronica ed Informazione Politecnico di Milano Piazza Leonardo da Vinci, Milano - Italy Filippo Ramoni ramoni@elet.polimi.it