Autonomic computing aims to address the increasing complexity and unmanageable nature of modern computing systems by creating self-managing environments that hide complexity from users. Key features include self-healing, self-configuring, self-protecting, and self-optimizing capabilities, enabling automated management and security across diverse platforms. It combines elements of biological systems and offers a solution to the issues faced in system management and software engineering.

1. Autonomic Computing
Presented by:Jaspreet Singh (07511502710)

3. Why Autonomic Systems ?
Technological advances
High Growth
More Complex Systems
Brittle, Unmanageable, Insecure
System and Application
Complexity Growth

4. Concept Derived from Biology
v Strategies based on biological systems
v Inspired by Autonomic Nervous System in the human body.
Defined as : - A self managing autonomous and ubiquitous computing environment that
completely hides its complexity, thus providing the user with an interface that exactly
meets his/her needs.

5. Four aspects of self-management
often cited by IBM

6. Self Healing

7. Self Configuring
Current Computing
Corporate Data centers have multiple
vendors and platforms. Installing, configuring
and integrating systems is time consuming
and error prone.
Autonomic Computing
Automated confihuration of components and
systems follows high level policies. Rest of
system adjusts automatically and seamlessly.

8. Self Protecting
Anticipate, detect, identify and protect
against attacks from anywhere.
- Defining and managing user
access to all computing rsources.
- Protecting against unauthorized
resource access, e.g. SSL
- Detecting intrusions and repoting
as they occur.

9. Self Optimizing
Monitor and tune resources
automatically
- support operating in
unpredictable environment.
- Efficient maximization of
resource utilization without
human
interference.
Dynamic resource allocation and
management.
- Resource: Storage, databases,
networks.

10. How it Works ?
Autonomic Elements: Structure
Fundamental atom of the architecture
Managed element(s)
Database, storage system, server, software
app, etc.
Plus one autonomic manager
Responsible for:
Providing its service
Managing its own behavior in accordance with
policies
Interacting with other autonomic elements
Autonomic Manager
Analyze
Monitor
Plan
Knowledge
S
E
Managed Element
Execute

11. Autonomic Elements: Interactions
Relationships
Dynamic, ephemeral, opportunistic
Defined by rules and constraints
Formed by agreement
May be negotiated
Full spectrum
Peer-to-peer
Hierarchical
Subject to policies

12. Autonomic Systems: Composition of Autonomic Elements
Workload
Manager
Arbiter
Planner
Provisioner
Broker
Server
Network
Sentinel
Workload
Manager
Network
Event
Correlator
Monitor
Server
Database
Server
Sentinel
Database
Monitor
Broker
Negotiator
Aggregator
Registry
Registry
Reputation
Authority
Storage
Storage
Storage
Monitor

14. Conclusion
• Solution of today’s increasing complexity in computing
science
Self-Management and dynamic adaptive behaviors
• Still challenges in diverse fields of science and
technology
– Autonomic behavior in one field of science
System managements, software engineering, etc.
– Needs for a abstraction and co-operation in relevant fields.
Autonomic computing is Solution of today’s increasing
complexity in computing science

15. Thank
You