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  • “ A collection of application services spread over networked computers, which clients use remotely via middleware services” Application services: represent physical and logical concepts such as a printer (hardware service) and a chat room (software service) Middleware services: bridge the gap between application services and network operating system to make life easier for application programmers including; Lookup, transaction, remote-event, etc. 1-reflection techniques are similar to self-adaptive software but self-adaptation exceeds the capabilities of reflection. (“ reflection can modify themselves at run time and change their behaviours but it can’t determine when and what the program needs to modify itself in run time”) 2- one of the key architecture concepts for self-adaptive software is a “reconfiguration “ which refer to a system that switches the control regime based on the runtime situation 3-As a new modelling paradigm self-adaptive software approaches provides two interactions; namely feedforward process from model to the executable and feedback process from execution to reconfiguration 4- it generalize software engineering as its adaptive control in the sense that the system will switch to a different algorithm when the environment changes. Gauges for dynamic assembly and adaptation Model -- Implementation mapping / Transformation (During) Run-time performance adaptation (After) Requirements / Event-based architectural models (Before) Architecture Transformation Technology Ontology-based gauges Open Points Constraint Gauges Reconfiguration Cost Dependability (for Security) Runtime Event Monitoring Run-time Configuration Connectivity Early Warning Evolution and Integration Command Center Performance-based Architectural Adaptation Architecture-driven Dynamism


  • 1. A Machine Learning Middleware Service for On-Demand Grid Services Engineering and Support Prof. A. Taleb-Bendiab School of Computing Liverpool John Moores University email: [email_address] http://www.cms.livjm.ac.uk/taleb http://www.cms.livjm.ac.uk/Self- X
  • 2. Scope
    • Situated Autonomic Computing
      • Problem Definition - Challenges
        • Design including; coordination and control
          • model-based vs emergence
      • Specification of control models
        • Design via experimentation and machine learning
        • Example – on-demand reservation of application services
          • User Classification scenario
          • Episodic resource requirements
      • SOM Classification for Connected Home Machine
    • Implementation
    • Case-study
  • 3. Situated AC Scenario: E-Fire Services
  • 4. Challenges -- Global Computing
    • Global Enterprise Systems
      • High-assurance systems development and life-time management
      • Complexity and scale is rapidly increasing
    • Bio-inspired Models -- Autonomy
      • devolving software management, maintenance to the software itself
        • Self-managing, self-tuning, self-protecting, ...
        • Need continuous measurement, introspection to support
          • Observed and/or supervised adaptation for;
          • Safe, predictable,
          • Coordinated, traceable, etc.
  • 5. So far …!
    • Current research
      • Instrumentation middleware services for
        • improved usability and reliability for instance for
          • grid-based applications, and ubiquitous computing
      • Monitor, control and manage grid users’ applications.
      • Context-awareness and QoS-Aware systems
      • Event-based systems
      • Sensor networks, Etc.
    • Further research is required
      • Management, assurance and fidelity of awareness layer is a major concerns
        • Sensors and actuators (effectors) support web services and grid computing
        • Current models looking at small scale systems
  • 6. Design Approach Informed by Machine Learning
    • Frameworks and Models
      • Programming, interaction and/or control models.
    • Two experiments were conducted
      • User Classification and on-demand service reservation
      • Autonomic software restore service
  • 7. Experiment #1: User Classification
    • The scenario
      • Mining service usage models per class of users for preemptive service reservation and on-demand services
    • Method
      • Developed an Simulation tool for Intelligent Connected Home, which generate services
      • Self-Organising Maps (SOM) applied extract feature or usage model
    • Design and Implementation
      • To follow
  • 8. Design and Implementation
    • Data generated tool is developed to produce training and test data for this application.
    • An OGSA and web service compliant SOM middleware service was developed
      • For rapid prototyping a Matlab library for SOM is used for classification
  • 9. SOM Classification Results For Connected Home Machine Devices
      • Lights and PlayStationII correlates
      • Video and Coffee Machine correlates
      • Video CD and Fans correlates
      • Vacuum cleaner and Washing machine correlates
  • 10. ML Middleware Services
  • 11. So What ?
    • Exploiting ML:
      • anticipate and organize the consumers’ requests in advanced.
      • Job schedule is responsible for managing the invocations of the services.
      • Just-in-time services invocation and usage
      • Etc.
    • In addition to the presented ML middleware service with automated inclusion and use of usage model for user and service classification
    • Further support is required including;
      • Specification and modelling of mined models and their enactment for instance;
        • Control and/or actuation models
      • Neptune Meta-Language and Integrated development environment will be used for this.
  • 12. Neptune Meta-Language #1
  • 13. Neptune Meta-Language #2
  • 14. Neptune Meta-Language #3
  • 15. Conclusions & Further Work
    • Prototypes developed using .Net and support Web Services Standards
    • Tested in a number of case studies
      • Intelligent Connected Homes
      • E-Health
      • With PlanetLab environment
    • Further work
      • Integration of this work with the Neptune Language to support
        • norm-governed web services and architectures.
        • Situated Autonomic middleware
      • Integration machine learning services to support danger/novelty detection
      • Further evaluation of the framework
  • 16. THANK YOU