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An IBM initiative for developing self-configuring computing systems

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  1. 1. Autonomic Computing Under the guidance of Prof.D.A.Noola Presented by: Kaushik Patidar 1
  2. 2. Contents • • • • • • • • • • Introduction Contemporary Glitches Autonomic Nervous System Conceptual framework and Architecture Characteristics Contemporary and Autonomic systems comparison Research Issues and Challenges Benefits Conclusion References 2
  3. 3. Introduction A software system that operates on its own or with a minimum of human interference according to a set of rules To increase productivity while minimizing complexity for users, capable of running themselves and adjusting to varying circumstances Control theory, adaptive algorithms, software agents, robotics, fault-tolerant computing, machine learning, artificial intelligence, and many more 3
  4. 4. Contemporary Glitches 4
  5. 5. The Growing Complexity Problem • Average increase of computing devices 38% per annum • Ratio of Labour costs to Equipment costs around 18:1 • Manual control is time-consuming, expensive, and errorprone • The distributed, diverse applications heterogeneous tasks deal with 5
  6. 6. The Evolution Problem • Maintenance and Evolutions of critical and legacy systems • Keep the values of quality attributes within desired ranges • Monitor or verify requirements (functional or nonfunctional) over long periods of time • Adapt safety-critical systems without halting them 6
  7. 7. Autonomic Human Nervous System • • A homeostatic system is an open system that maintains its structure and functions by means dynamic equilibriums Rigorously controlled by interdependent regulation mechanisms • A series of modifications that are equal in size and opposite in direction to those that created the disturbance • The activities to maintain blood glucose level is an tremendous example 7
  8. 8. Blood-Glucose Concentration Regulation Glucose Concentration in Blood (in percentage) Activities Less than 0.06 Tissue Starvations Liver converts Glycogen to Glucose More than normal Pancreas secretes Insulin Muscles and Skin disposes the excess Greater than 0.18 Kidney excretes excess into urine 8
  9. 9. Ashby’s Ultrastable System • The goal of the adaptive behaviour is the survivability of the system • The system will always work towards returning to the original equilibrium state in case of disturbances:o Frequent small impulses to the main variables o Occasional step changes to its parameters Fig. Essential variables 9
  10. 10. Ashby’s Ultrastable System Fig. The Ultra-Stable system architecture 10
  11. 11. Human Nervous System as an Ashby’s Ultrastable System Fig. Nervous system as part of an Ultrastable system 11
  12. 12. Conceptual Model • A system that operates and serves its purpose by managing itself without external intervention even in case of environmental changes Fig. Conceptual prototype of Autonomic System 12
  13. 13. Architecture • A closed control loop in a self-managing system monitors some resource and autonomously tries to keep its parameters within a desired range Desired Range ? Control Resource Measure Fig. Control Loop 13
  14. 14. Architecture(contd.) • Autonomic element Fig. Autonomic elements 14
  15. 15. Architecture(contd.) Fig. The Sensors and Effectors in an Autonomic Element 15
  16. 16. Interface Standards 16
  17. 17. Characteristics  Focal Characteristics: Fig. Autonomic System Characteristics 17
  18. 18. Miscellaneous Characteristics Other recommended attributes include • Automaticity • Adaptive • Aware • Reflexivity • Transparency • Open Source • Autonomicity and Evolvability • Easy to train and learn 18
  19. 19. An ode to Policies • Policies are a form of guidance used to determine decisions and actions Fig. States and Actions • Action, Goal and Utility policies are its types 19
  20. 20. Contemporary versus Autonomic Computing Concept Current Computing Autonomic Computing Self-configuration Time consuming and error prone System adjusts automatically or by policies Self-optimization Manually set, ever increasing parameters Improve their own performance and efficiency Self-healing Few weeks to solve problems System automatically detects, diagnoses, and repairs problems Self-protection Manual detection of and recovery System automatically defends from attacks against malicious attacks 20
  21. 21. Inoculation of Autonomicity in Software Fig. Increasing Autonomic Functionality 21
  22. 22. Research Issues and Challenges 22
  23. 23. Benefits of Autonomic Computing Short-term I/T related benefits • • • • • • • • Simplified user experience Cost-savings - scale to use Scaled power, storage and costs that optimize h/w & s/w usage Full use of idle processing power Natural language queries allow deeper and more accurate returns Seamless access to multiple file types Stability, High availability. High security system Fewer system or network errors due to self-healing 23
  24. 24. Benefits of Autonomic Computing (contd.) Long-term, Higher Order Benefits • • • • • Realize the vision of enablement by shifting available resources to higherorder business Embedding autonomic capabilities in client or access devices, servers, storage systems, middleware, and the network itself. Constructing autonomic federated systems Achieving end-to-end service level management Collaboration and global problem-solving Massive simulation and complex calculations which require processors to run 24X7 for as long as a year at a time 24
  25. 25. Applications • • Systems that incorporate autonomic mechanisms for problem determination, monitoring, analysis, management, etc Examples: 25
  26. 26. Applications(contd.) Systems to support the development of autonomic systems and applications • Examples : • 26
  27. 27. Conclusions • Users demand and crave simplicity in computing solutions • A system used by millions of people each day and administered by a half-time person seems attainable with the notion of automatic updates • It will take another decade for the proliferation of Autonomicity in existing systems 27
  28. 28. References [1] IBM Corporation. An architectural blueprint for autonomic computing. April 2003. [2] Manish Parashar and Salim Hariri , Autonomic Computing: An Overview, The Applied Software Systems Laboratory, Rutgers University, Piscataway NJ, University of Arizona, Tucson, AZ, USA. [3] Hausi A. Müller, Liam O’Brien, Mark Klein and Bill Wood , Autonomic Computing, April 2006 [4] IBM, “Autonomic Computing: IBM’s Perspective on the State of Information Technology”, /resource /thought/278606109.html. [5] IEEE Computer Magazine, Jan 2003 28
  29. 29. Any Queries ? 29
  30. 30. Thank You 30