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Distributed Autonomic Management (DAM). Nitin Bande . Introduction DAM concept DAM Approach Related Work Summary Conclusion. Introduction. Current approaches to network model employ client/server model, non-intelligent mobile agents.

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Presentation Transcript
slide2

Introduction

  • DAM concept
  • DAM Approach
  • Related Work
  • Summary
  • Conclusion
slide3

Introduction

  • Current approaches to network model employ client/server model, non-intelligent mobile agents.
  • Autonomic Computing: Initiative was proposed by IBM in 2001
    • Inspired by biological systems such as the autonomic human nervous system
    • Deal with complexity, dynamism, heterogeneity and uncertainty
    • significant new strategic and holistic approach to the design of complex distributed computer systems
    • Provides the user with an interface that exactly meets her/his needs
slide4

DAM Concepts

  • Distributed Autonomic Computing & Autonomic Nervous System
  • Human Nervous system controls the vegetative functions of the body
  • Many decisions made by autonomic elements in body are involuntary
  • Biological self-management is influenced for developing self-management within the systems
  • Distinction between autonomic activity in the human body and autonomic responses in computer systems
  • to cope with the rapidly growing complexity of integrating, managing, and operating computing system
  • Autonomic elements in computer systems make decisions based on tasks which are chosen to be delegated to the technology
slide5

Distributed Autonomic Computing

  • Characteristics
      • Self-Configuration : Ability of the system to automatically adapt to changes
      • Self-Healing : Ability of the system to discover, diagnose & react to disruptions
      • Self-Optimization : Ability of the system to maximize the resource utilization
      • Self-Protection : Ability of the system to detect, diagnose & act to prevent disruptions
      • Self-Awareness : System is aware of its states and behavior
  • Benefits
      • Efficiency, Maintainability, Functionality, Reliability, Usability, and Portability
slide6

DAM Reference Model

  • Managed elements
  • Sensors
  • Autonomic Manager
  • Effectors
slide7

AI & DAM Components

  • Soft computing techniques
  • Neural networks, fuzzy logic, probabilistic reasoning incorporating and so on
  • Machine learning techniques, optimization techniques, fault diagnosis techniques, feedback control, and planning techniques
  • Clockwork
  • A method provides predictive self-management, regulates behavior in anticipation of need using statistical modeling, tracking and forecasting methods, Self-configuration element
  • Probabilistic technique
  • Autonomic algorithm selection
  • Self-training and self-optimization to find the best algorithm
slide8

Large-scale server management and control

  • Time-series methods, rule-based classification for a self- management and control system
  • Calculation of costs in an autonomic system and the self- healing equation
  • Machine design
  • Reaction
  • The lowest level where no learning occurs and only involves immediate response to state information coming from sensory systems
  • Routine
  • Middleware level where largely routine evaluation and planning behaviors take place
  • Reflection
  • Top level, which receives input from below
  • Meta process, where the mind deliberates about itself
slide9

DAM Approaches

  • Stationary intelligent agent approach & Mobile agent approach
  • Endowing traditional SNMP agents that were essential in the client/server model with some form of intelligence.
  • Collaboration of SNMP agents with mobile agents.
  • Bandwidth problem is the primary focus.
  • Detection of faults and performance degradations of distributed networks.
  • Agents may share knowledge by sharing cases or by sharing adaptation procedures.
slide10

Challenges of DAM

  • Identification and accessibility
  • Analyze monitored data
  • Self-configuration in large scale applications
  • Problem localization
  • Decision making
  • Self protecting against active threats
slide11

Conclusion

  • Inspired by biological systems such as the autonomic human nervous system
  • Enables the development of self-managing computing systems and applications
  • The systems/applications use autonomic strategies and algorithms to handle complexity and uncertainties with minimum human intervention
  • Implement intelligent control loops to monitor, analyze, plan and execute using knowledge of the environment
  • Challenge is be accomplished through a combination of process changes, skills evolution, new technologies and architecture, and open industry standards