A concise introduction to autonomic computing Roy Sterritt, Manish Parashar, Mhuaglory Tianfield, Ra

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A concise introduction to autonomic
computingRoy Sterritt, Manish Parashar,
Mhuaglory Tianfield, Rainer UnlandJournal of
Advanced Engineering Informatics, Engineering
Applications of Artificial Intelligence, Special
Issue on Autonomic Computing and Automation,
Elsevier Publishers, vol. 19, July 2005, pp.
181-187
Date September 19th 2007 Joon-Myung
Kang eliot_at_postech.ac.kr DPNM Lab., Dept. of
CSE, POSTECH
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Presentation Outline

• Introduction
• Concepts
• Autonomic Computing
• Examples of autonomic systems and applications
• Conclusion

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Introduction

• Autonomic Computing
• Alternative approaches based on strategies used
  by biological systems
• Deal with complexity, dynamism, heterogeneity and
  uncertainty
• significant new strategic and holistic approach
  to the design of complex distributed computer
  systems
• Autonomic System
• Self-managing autonomous and ubiquitous computing
  environment that completely hides its complexity
• Provides the user with an interface that exactly
  meets her/his needs
• Self-management (self- properties)
• Self-governing, self-adaptation,
  self-organization, self-optimization,
  self-configuration, self-diagnosis of faults,
  self-protection, self-healing, self-recovery, and
  autonomy

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Concepts

• Autonomic Nervous System
• The part of the nervous system that controls the
  vegetative functions of the body
• Biological self-management is influencing a new
  paradigm for computing that aims to create
  similar self-management within the systems

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Concepts

• Autonomic computing systems
• In 2001, IBM introduced the autonomic computing
  initiative with the aim of developing
  self-managing systems
• Evolution to cope with the rapidly growing
  complexity of integrating, managing, and
  operating computing system
• Distinction between autonomic activity in the
  human body and autonomic responses in computer
  systems
• Many of the decisions made by autonomic elements
  in the body are involuntary
• Autonomic elements in computer systems make
  decisions based on tasks which are chosen to be
  delegated to the technology

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Concepts

• Autonomic Computing
• Four general properties for self-management
• Self-configuring
• Self-healing
• Self-optimising
• Self-protecting
• Four enabling properties or attributes
• Self-awareness
• Environment-awareness
• Self-monitoring
• Self-adjusting
• Self-
• Self-anticipating, self-adapting, self-critical,
  self-defining, self-destructing, self-diagnosis,
  self-governing, self-organized, self-recovery,
  self-reflecting, and self-simulation,
  self-stabilizing

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Concepts

• Autonomic Computing
• To meet this autonomic selfware vision, systems
  should be designed with components that are
  allocated an autonomic manager
• sensors (self-monitor), effectors (self-adjuster)
• The vision behind the initiative has as its
  overarching goal system-wide policy-based
  self-management, where a human manager will state
  a business critical success factor

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Autonomic Computing

• Innovative self-managing components and
  interaction
• The Autonomic Environment requires that autonomic
  elements and in particular autonomic managers
  communicate with one another concerning self-
  activities
• PBM (pulse monitor)
• An extension of the embedded systems heart-beat
  monitor
• The capability to encode health and urgency
  signals as a pulse

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Autonomic Computing

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

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Autonomic Computing

• AI and autonomic components
• Root cause analysis
• Correlation, rule discover and root cause
  analysis activity
• Large-scale server management and control
• Time-series methods, rule-based classification
  and Bayesian network algorithms for a
  self-management and control system
• Calculation of costs in an autonomic system and
  the self-healing equation
• Naïve Bayes for cost-sensitive classification and
  a feedback approach based on a Markov decision
  process for failure remediation
• Machine design
• Reaction
• The lowest level where no learning occurs and
  only involves immediate response to state
  information coming from sensory systems
• Routine
• Middleware leel where largely routine evaluation
  and planning behavirours take place
• Reflection
• Top level, which receives input from below
• Meta process, where the mind deliberates about
  itself

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Autonomic Computing

• Autonomic Architecture
• IBM view
• Represents the closed control loop within the
  autonomic element as consisting of four stages
• MAPE-monitor, analyze, plan and execute
• MAPE components use correlations, rules, beliefs,
  expectation, histories and other information
  known to the autonomic element, or available to
  it through the knowledge repository within the AM

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Autonomic Computing

• Autonomic interaction and policy based
  self-management
• Support inter-element interactions, such as
  service-level agreements, negotiation protocols
  and algorithms, and conversation support
• Policy based management becomes particularly
  important with the future vision of Autonomic
  Computing, to facilitate high level specification
  of the goals and aims for the system to achieve
• Computer-human interaction and Autonomic Systems
• User studies, interfaces for monitoring and
  controlling behaviour, and techniques for
  defining, distributing, and understanding
  policies

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Autonomic Computing

• Systems and software engineering for Autonomic
  Systems
• How to program autonomic systems
• How to design for self-management
• How to gather requirements specifically for an
  autonomic environment
• AutoMate
• Autonomic component framework utilizes DIOS to
  provide mechanisms to directly enhance
  traditional computational objects/components with
  sensors, actuators, rules, a control network,
  management of distributed sensors and actuators,
  interrogation, monitoring and manipulation of
  components at runtime through a distributed
  rule-engine DIOS Distributed Object
  Infrastructure for Interaction and Steering

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Autonomic Computing

• Eight defining characteristics P. Horn.
  Autonomic Computing IBMs perspective on the
  State of Information Technology.
  http//www.research.ibm.com/autonomic/, Oct 2001.
  IBM Corp.
• Self Awareness An autonomic application/system
  knows itself and is aware of its state and its
  behaviors
• Self Configuring An autonomic application/system
  should be able ocnfigure and reconfigure itself
  under varying and unpredictable conditions
• Self Optimizing An autonomic application/system
  should be able to detect suboptimal behaviors and
  optimize itself to improve its execution
• Self-Healing An autonomic application/system
  should be able to detect and recover from
  potential problems and continue to function
  smoothly

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Autonomic Computing

• Eight defining characteristics P. Horn.
  Autonomic Computing IBMs perspective on the
  State of Information Technology.
  http//www.research.ibm.com/autonomic/, Oct 2001.
  IBM Corp.
• Self Protecting An autonomic application/system
  should be capable of detecting and protecting its
  resources from both internal and external attack
  and maintaining overall system security and
  integrity
• Context Aware An autonomic application/system
  should be aware of its execution environment and
  be able to react to changes in the environment
• Open An autonomic application/system must
  function in an heterogeneous world and should be
  portable across multiple hardware and software
  architectures. Consequently it must be built on
  standard and open protocols and interfaces
• Anticipatory An autonomic application/system
  should be able to anticipate to the extent
  possible, its needs and behaviors and those of
  its context, and be able to manage itself
  proactively

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Examples
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Examples

• Systems supporting development of autonomic
  applications and systems

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Conclusion

• Summary
• Introduced the autonomic computing paradigm,
  which is inspired by biological systems such as
  the autonomic human nervous system
• Autonomic Computing 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
• Autonomic application/system is a collection of
  autonomic elements, which implement intelligent
  control loops to monitor, analyze, plan and
  execute using knowledge of the environment
• Achieving overall autonomic behaviors remains an
  open and significant challenge, which will be
  accomplished through a combination of process
  changes, skills evolution, new technologies and
  architecture, and open industry standards