1. Prof. A. Taleb-Bendiab, talk: WO, Conference: ICIES’05, Miami, Date:Prof. A. Taleb-Bendiab, talk: WO, Conference: ICIES’05, Miami, Date: 01/28/1501/28/15, Slide:, Slide: 11
A Machine Learning
Middleware Service for On-
Demand Grid Services
Engineering and Support
Prof. A. Taleb-BendiabProf. A. Taleb-Bendiab
School of ComputingSchool of Computing
Liverpool John Moores UniversityLiverpool John Moores University
email:email: a.talebbendiab@livjm.ac.uka.talebbendiab@livjm.ac.uk
http://www.cms.livjm.ac.uk/talebhttp://www.cms.livjm.ac.uk/taleb
http://www.cms.livjm.ac.uk/Self-http://www.cms.livjm.ac.uk/Self-XX

2. ScopeScope
 Situated Autonomic ComputingSituated Autonomic Computing
 Problem Definition - ChallengesProblem Definition - Challenges
 Design including; coordination and controlDesign including; coordination and control
 model-based vs emergencemodel-based vs emergence
 Specification of control modelsSpecification of control models
 Design via experimentation and machine learningDesign via experimentation and machine learning
 Example – on-demand reservation ofExample – on-demand reservation of
application servicesapplication services
 User Classification scenarioUser Classification scenario
 Episodic resource requirementsEpisodic resource requirements
 SOM Classification for Connected HomeSOM Classification for Connected Home
MachineMachine
 ImplementationImplementation
 Case-studyCase-study

3. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 3
Situated AC Scenario: E-FireSituated AC Scenario: E-Fire
ServicesServices

4. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 4
Challenges -- Global ComputingChallenges -- Global Computing
 Global Enterprise SystemsGlobal Enterprise Systems
 High-assurance systems development and life-High-assurance systems development and life-
time managementtime management
 Complexity and scale is rapidly increasingComplexity and scale is rapidly increasing
 Bio-inspired Models -- AutonomyBio-inspired Models -- Autonomy
 devolving software management, maintenance todevolving software management, maintenance to
the software itselfthe software itself
 Self-managing, self-tuning, self-protecting, ...Self-managing, self-tuning, self-protecting, ...
 Need continuous measurement, introspectionNeed continuous measurement, introspection
to supportto support
 Observed and/or supervised adaptation for;Observed and/or supervised adaptation for;
 Safe, predictable,Safe, predictable,
 Coordinated, traceable, etc.Coordinated, traceable, etc.

5. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 5
So far …!So far …!
 Current researchCurrent research
 Instrumentation middleware services forInstrumentation middleware services for
 improved usability and reliability for instance forimproved usability and reliability for instance for
 grid-based applications, and ubiquitousgrid-based applications, and ubiquitous
computingcomputing
 Monitor, control and manage grid users’ applications.Monitor, control and manage grid users’ applications.
 Context-awareness and QoS-Aware systemsContext-awareness and QoS-Aware systems
 Event-based systemsEvent-based systems
 Sensor networks, Etc.Sensor networks, Etc.
 Further research is requiredFurther research is required
 Management, assurance and fidelity of awarenessManagement, assurance and fidelity of awareness
layer is a major concernslayer is a major concerns
 Sensors and actuators (effectors) support webSensors and actuators (effectors) support web
services and grid computingservices and grid computing
 Current models looking at small scale systemsCurrent models looking at small scale systems

6. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 6
Design Approach Informed byDesign Approach Informed by
Machine LearningMachine Learning
 Frameworks and ModelsFrameworks and Models
 Programming, interaction and/or controlProgramming, interaction and/or control
models.models.
 Two experiments were conductedTwo experiments were conducted
 User Classification and on-demand serviceUser Classification and on-demand service
reservationreservation
 Autonomic software restore serviceAutonomic software restore service

7. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 7
Experiment #1: User ClassificationExperiment #1: User Classification
 The scenarioThe scenario
 Mining service usage models per class ofMining service usage models per class of
users for preemptive service reservation andusers for preemptive service reservation and
on-demand serviceson-demand services
 MethodMethod
 Developed an Simulation tool for IntelligentDeveloped an Simulation tool for Intelligent
Connected Home, which generate servicesConnected Home, which generate services
 Self-Organising Maps (SOM) applied extractSelf-Organising Maps (SOM) applied extract
feature or usage modelfeature or usage model
 Design and ImplementationDesign and Implementation
 To followTo follow

8. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 8
Design and ImplementationDesign and Implementation
 Data generated tool is developed toData generated tool is developed to
produce training and test data for thisproduce training and test data for this
application.application.
 An OGSA and web service compliantAn OGSA and web service compliant
SOM middleware service wasSOM middleware service was
developeddeveloped
 For rapid prototyping a Matlab library forFor rapid prototyping a Matlab library for
SOM is used for classificationSOM is used for classification

9. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 9
SOM Classification Results ForSOM Classification Results For
Connected Home Machine DevicesConnected Home Machine Devices
• Lights and PlayStationII correlates
• Video and Coffee Machine correlates
• Video CD and Fans correlates
• Vacuum cleaner and Washing machine correlates

10. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 11
So WhatSo What??
 Exploiting ML:Exploiting ML:
 anticipate and organize the consumers’ requests inanticipate and organize the consumers’ requests in
advanced.advanced.
 Job schedule is responsible for managing theJob schedule is responsible for managing the
invocations of the services.invocations of the services.
 Just-in-time services invocation and usageJust-in-time services invocation and usage
 Etc.Etc.
 In addition to the presented ML middleware service withIn addition to the presented ML middleware service with
automated inclusion and use of usage model for userautomated inclusion and use of usage model for user
and service classificationand service classification
 Further support is required including;Further support is required including;
 Specification and modelling of mined models and theirSpecification and modelling of mined models and their
enactment for instance;enactment for instance;
 Control and/or actuation modelsControl and/or actuation models
 Neptune Meta-Language and Integrated developmentNeptune Meta-Language and Integrated development
environment will be used for this.environment will be used for this.

11. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 12
Neptune Meta-Language #1Neptune Meta-Language #1

12. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 13
Neptune Meta-Language #2Neptune Meta-Language #2

13. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 14
Neptune Meta-Language #3Neptune Meta-Language #3

14. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 15
Conclusions & Further WorkConclusions & Further Work
 Prototypes developed using .Net and supportPrototypes developed using .Net and support
Web Services StandardsWeb Services Standards
 Tested in a number of case studiesTested in a number of case studies
 Intelligent Connected HomesIntelligent Connected Homes
 E-HealthE-Health
 With PlanetLab environmentWith PlanetLab environment
 Further workFurther work
 Integration of this work with the Neptune Language toIntegration of this work with the Neptune Language to
supportsupport
 norm-governed web services and architectures.norm-governed web services and architectures.
 Situated Autonomic middlewareSituated Autonomic middleware
 Integration machine learning services to supportIntegration machine learning services to support
danger/novelty detectiondanger/novelty detection
 Further evaluation of the frameworkFurther evaluation of the framework

15. Prof. A. Taleb-Bendiab, Talk: WO, CSAC’05 Workshop, Conference: ICIES’05, Miami,
Date: 01/28/15, Slide: 16