Welcome to DEAS 2005 Design and Evolution of Autonomic Application Software David Garlan, CMU Marin Litoiu, IBM CAS Hausi A. Müller, UVic John Mylopoulos, UofT Dennis B. Smith, SEI Kenny Wong, UofA

2 Agenda  9:10 – 10:30 Self-healing  David Garlan, CMU  10:30 – 11:00 Nutrition Break  11:00 – 12:30 Applications  Marin Litoiu, IBM  12:30 – 2:00 Lunch Break  2:00 – 3:30 Requirements  Dennis Smith, SEI  3:30 – 4:00 Nutrition Break  4:00 – 5:30 Interoperability  Ken Wong, UofA Sessions 20 mins per talk 1 hours for 3 talks 30 mins discussion break

3 Proceedings  21 papers  131 pages  To appear in ACM Digital Library  Copyright ACM /05/0005  One paper did not make it into the proceedings due to copyright issues, but a copy of the paper is available here  Thank you for submitting the copyright forms   Bookmarks fixed

4 What is Autonomic Computing?  Self-managed systems …  Systems that self-manage  self-configure, self-tune, self-repair, self-protect, self-…

5 What is Autonomic Computing?  Webster’s definition  Acting or occurring involuntarily; automatic: an autonomic reflex  Relating to, affecting, or controlled by the autonomic nervous system or its effects or activity  Autonomic nervous system: that part of the nervous system that governs involuntary body functions like respiration and heart rate  IBM’s definition  An approach to self-managed computing systems with a minimum of human interference  The term derives from the body's autonomic nervous system, which controls key functions without conscious awareness or involvement

6 Most famous Autonomic System

7 Characteristics of Autonomic Computing Systems  Reflexivity, identity  Possesses a system identity  Must know itself  Needs detailed knowledge of its components, current status, interconnections with other systems and available resources to manage itself  Able to configure and reconfigure itself under varying and unpredictable conditions  For example, adaptive algorithms running on each learn the best configurations to deliver functionality in different ways to achieve mandated performance  Continually seek to optimize its operations  Adaptive algorithms for monitoring and execution

8 Characteristics of Autonomic Computing Systems  Able to recover—without loss of data or noticeable delays in processing—from events that might cause some of its parts to malfunction  Recovery mechanisms  At the system and application layer  Human effort supplemented with self-learning algorithms  Capable of protecting themselves  Using pattern recognition to detect and deter threats  Aware of and adaptive to environment and context  Technology independent control theory

9 Characteristics of Autonomic Computing Systems  Able to function in a heterogeneous world based on open standards  Web services, control and data integration; connecting sensors and actuators  Complex heterogeneous infrastructures are a reality  Perform in an environment where computer resources are shared (e.g., in a distributed, grid-like manner)  Carry out various functions and anticipate the resources needed while keeping complexity hidden

10 Characteristics of Autonomic Computing Systems  A software system is autonomic, if it supports behaviors in the following range  Self-configuring — choosing a suitable behaviour, based on user preferences, context, …  Self-tuning — choosing behaviors that optimize certain qualities (performance, year-end profits, …)  Self-repairing — shifting execution to another behaviour, given that the current one is failing  Self-protecting — choosing a behaviour that minimizes risks (attacks, viruses, …)

11 Increased Responsiveness Adapt to dynamically changing environments Business Resiliency Discover, diagnose, and act to prevent disruptions Operational Efficiency Tune resources and balance workloads to maximize use of IT resources Secure Information and Resources Anticipate, detect, identify, and protect against attacks Autonomic computing attributes Self-managing systems that deliver

12  An autonomic manager contains a continuous control loop that monitors activities and takes actions to adjust the system to meet business objectives  Autonomic managers learn from past experience to build action plans  Elements need to be instrumented consistently, based on open standards Self-management and autonomic managers

13 Level 2Level 3Level 4Level 5Level 1 Basic Managed Predictive Adaptive Autonomic Manual analysis and problem solving Centralized tools, manual actions Cross-resource correlation and guidance System monitors, correlates and takes action Dynamic business policy based management Evolution not revolution Levels of autonomic maturity

14 Agenda  9:10 – 10:30 Self-healing  David Garlan, CMU  10:30 – 11:00 Nutrition Break  11:00 – 12:30 Applications  Marin Litoiu, IBM  12:30 – 2:00 Lunch Break  2:00 – 3:30 Requirements  Dennis Smith, SEI  3:30 – 4:00 Nutrition Break  4:00 – 5:30 Interoperability  Ken Wong, UofA Sessions 20 mins per talk 1 hours for 3 talks 30 mins discussion break

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