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SelfConfiguration of Adaptive Ubiquitous Applications

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Title: SelfConfiguration of Adaptive Ubiquitous Applications


1
Self-Configuration of Adaptive Ubiquitous
Applications
  • Prof. Dr. Kurt Rothermel
  • Dr. Christian Becker
  • Marcus Handte

2
Outline
  • Motivation
  • Project Status
  • Configuration
  • Integration
  • Summary
  • Outlook

3
Motivation
  • Ubiquitous Computing
  • Distraction-free support for tasks
  • Distributed applications
  • Dynamic environments (mobility)
  • ? Applications need to adapt
  • A common approach
  • Applications as compositions of
    services/components
  • Application composition needs to be configured at
    runtime
  • Research goal
  • Design a lightweight algorithm that
    (re-)configures applications in
    resource-restricted environments taking into
    account the resulting costs and potential quality
    gains using a simple quality metric

Universal Messenger
4
Project Overview
  • Determine a valid application configuration
  • Analyze the requirements of configuration
    algorithms
  • Design an algorithm that configures an
    application
  • Adapt an existing application configuration
  • Analyze adaptation costs and develop cost metric
  • Enhance previous approach to adapt configuration
    with minimal costs
  • Optimize an application configuration
  • Develop suitable quality metric for optimization
  • Extend algorithms to consider quality metric
  • Balance between adaptation cost and quality gains

5
Project Status
  • Determine a valid application configuration
  • Adapt an existing application configuration
  • Optimize an application configuration
  • ? Problem interpretation and extended
    backtracking algorithm
  • M. Handte, C. Becker, K. Rothermel Peer-based
    Automatic Configuration of Pervasive
    Applications, Journal on Pervasive Computing
    and Communications, 1(4), Troubador Publishing
    LTD, 251-264, December 2005
  • M. Handte, C. Becker, K. Rothermel Peer-based
    Automatic Configuration of Pervasive
    Applications, 2nd IEEE International
    Conference on Pervasive Services (ICPS) 2005,
    Santorini, Greece, 2005
  • Michael Reinsch, Entwicklung und Evaluation
    eines Algorithmus zur initialen Konfiguration von
    Anwendungen in PCOM, Diplomarbeit, Universität
    Stuttgart, Fakultät Informatik, Elektrotechnik
    und Informationstechnik, 2004

? Problem interpretation and extended
backtracking algorithm
  • ? Framework support for automatic adaptation of
    stateful components
  • ? Greedy cost optimization on top of initial
    backtracking algorithm
  • M. Handte, G. Schiele, S. Urbanski, C. Becker,
    Adaptation Support for Stateful Components in
    PCOM, Workshop on Software Architectures for
    Self-Organization Beyond Ad-Hoc Networking at
    3rd International Conference on Pervasive
    Computing (Pervasive) 2005, München, Germany,
    2005
  • Andreas Störzbach, Entwicklung und Evaluation
    eines Algorithmus zur Anpassung von Anwendungen
    in PCOM, Diplomarbeit, Universität Stuttgart,
    Fakultät Informatik, Elektrotechnik und
    Informationstechnik, 2005
  • ? Analysis of scenarios and design of level-based
    quality model
  • T. Weis, M. Handte, M. Knoll, C. Becker
    Customizable Pervasive Applications, 4th IEEE
    International Conference on Pervasive
    Computing and Communications (PERCOM) 2006, Pisa,
    Italy, 2006
  • Christian Schwegmann, Entwicklung eines
    Rahmenwerks zur Modellierung von
    Qualitätsmerkmalen für Konfigurationen in PCOM
    , Diplomarbeit, Universität Stuttgart, Fakultät
    Informatik, Elektrotechnik und
    Informationstechnik, 2005

6
System Model
  • Peer-to-Peer Approach
  • Devices in communication range cooperate
  • No supporting infrastructure required
  • PCOM Component System
  • Components with contractually specified
    dependencies towards other components and local
    resources
  • Container provides runtime environment for
    components and manages (potentially limited)
    local resources
  • Container provides basic services (e.g.
    communication, device discovery)
  • ? Applications as tree of component instances
    composed along dependencies

Input
Output
7
Automatic Configuration
File System (Input)
PDA
PPT Control (Application)
CPU
5
MEM
10
CPU
100
Imager (Display)
MEM
100
Input
Output
DISP
1
Laptop
Local PPT (Output)
CPU
5
MEM
10
Remote PPT (Output)
CPU
5
DISP
CPU
300
1
MEM
10
MEM
200
DISP
1
Display
Display
  • ? Composition that satisfies structural
    requirements (no unresolved dependencies) and
    resource requirements (no overloaded resources)

Executable Configuration
8
Requirements on Automatic Configuration
  • Completeness
  • Frustrated users if configuration sometimes not
    successful
  • Systematic search that finds a valid
    configuration eventually
  • Efficiency
  • Short configuration delays since users might wait
  • Applicable for a broad range of environments
  • Distribution
  • Do not rely on powerful server for configuration
  • Suitable for peer-based systems
  • Resilience
  • Deal with failures and mobility during
    configuration

9
Approach
  • Interpretation as Distributed Constraint
    Satisfaction Problem
  • Given Set of variables (distributed across
    agents) with finite domains and constraints
    between variable assignments
  • Find Valid variable assignment for all variables
  • Adapted Asynchronous Backtracking (Yokoo et al.
    97)
  • Distribution use available parallelism, no
    powerful device
  • Dependency-directed reconsiders only
    assignments that can resolve a conflict (conflict
    locality)
  • Extension for resilience integrated

10
Mapping to CSP
  • Dependencies as variables
  • Component instances as domains
  • Resource and structural requirements as
    constraints
  • Partial solution
  • Introduction of pseudo value
  • Constraints
  • If instance is used, dependencies must be
    resolved
  • If instance is not used, pseudo value must be
    selected
  • Instances on container must not overload resources

Input
Output
11
Evaluation
  • Simulation of binary tree with 12 components on 4
    containers
  • 1-30 components that introduce resource
    conflicts, 1 solution
  • 1 conflicting resource on each container (no
    conflict locality)

12
Integration
Components
  • Demonstrator Multihop Presenter
  • Pervasive Presenter(Component-based Application)
  • PCOM (Component System)
  • BASE (Object-oriented Middleware)
  • M. Handte, S. Urbanski, C. Becker, P. Reinhard,
    M. Engel, M. Smith 3PC/MarNET Pervasive
    Presenter, 4th IEEE International Conference on
    Pervasive Computing and Communications (PerCom
    2006), Pisa, Italy, 2006

PCOM Container
Semantic Service Matching
  • Semantic Service Matching (Jena)
  • Service Mgmt and Discovery (Aachen)
  • Mobility-aware Multicast (Karlsruhe)

Contract Framework
Configuration Algorithms
Resource Management
Efficient Service Discovery
BASE Micro-broker
Invocation Broker
Registries
  • Position-based Multicast (Mannheim)
  • Publish/Subscribe Middleware (Magdeburg)
  • Routing Emulation(Marburg)

Plugin Manager
Communication (Routing, Multicast)
Discovery Communication Plugins
...
Emulation
Network
13
Summary
  • Ubiquitous Computing requires self-configuring
    applications
  • Basis Basic abstractions for adaptive
    applications (PCOM)
  • Goal Provide suitable algorithms for automatic
    (re-)configuration
  • Approach Map configuration to CSP and extend
    existing algorithms
  • Status On schedule
  • Priority Programme Integration
  • Synergetic and complementary research
  • Emulation, Service Discovery, Multi-hop Routing,
    Multicast, Semantic Service Matching,
  • Demonstrator
  • Marburg, Mannheim, Magdeburg

14
Outlook
  • 3rd phase introduces new focus on heterogeneity
  • Device capabilities will be heterogeneous
    (resource-poor and rich)
  • Device mobility will be heterogeneous (static as
    well as mobile)
  • Users tend to reuse same applications in same
    environments
  • Research goal for 3rd phase
  • Leverage assumptions to reduce configuration
    delay
  • Proposed research direction
  • Dynamically distribute configuration process
    depending on resources
  • Enable dynamic distribution of configuration
    algorithms
  • Analyze the effects of various degrees of
    distribution
  • Derive and integrate distribution heuristic
  • Reuse partial application configurations in less
    dynamic scenarios
  • Analyze different models for partial
    configurations to foster efficient reuse
  • Enhance algorithms to enable usage of existing

15
More Resources
  • Web
  • www.3pc.info
  • Some references
  • C. Becker, M. Handte, G. Schiele, K. Rothermel
    PCOM A Component System for Pervasive
    Computing, 2nd IEEE International Conference on
    Pervasive Computing and Communication (PerCom),
    Orlando, USA , 2004
  • M. Handte, C. Becker, K. Rothermel Peer-based
    Automatic Configuration of Pervasive
    Applications, Journal on Pervasive Computing and
    Communications, 1(4), Troubador Publishing LTD,
    251-264, December 2005
  • T. Weis, M. Handte, M. Knoll, C. Becker
    Customizable Pervasive Applications, 4th IEEE
    International Conference on Pervasive Computing
    and Communications (PerCom), Pisa, Italy, 2006
  • E-Mail
  • kurt.rothermel christian.becker
    marcus.handte _at_ ipvs.uni-stuttgart.de
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