26-11-2010, 04:51 PM
AUTONOMIC COMPUTING.PPT (Size: 244 KB / Downloads: 148)
Presented by:PUNEETH KUMAR.N
AUTONOMIC COMPUTING
INTRODUCTION
Autonomic Computing is an initiative started by IBM
in 2001. Its ultimate aim is to develop computer systems
capable of self-management, to overcome the rapidly
growing complexity of computing systems management,
and to reduce the barrier that complexity poses to further
growth. In other words, autonomic computing refers to the
self-managing characteristics of distributed computing
resources, adapting to unpredictable changes while hiding
intrinsic complexity to operators and users.
Actually What do you mean by AUTONOMIC COMPUTING?
Autonomic computing is a self-managing computing model named after, and patterned on, the human body's autonomic nervous system. An autonomic computing system would control the functioning of computer applications and systems without input from the user, in the same way that the autonomic nervous system regulates body systems without conscious input from the individual. The goal of autonomic computing is to create systems that run themselves, capable of high-level functioning while keeping the system's complexity invisible to the user.
EIGHT BASIC CRITERIA THAT LEAD IBM FOR DEFINING A PERVASIVE AUTONOMIC COMPUTING SYSTEM
The system must be capable of taking continual stock of itself, its connections, devices and resources, and know which are to be shared or protected.
It must be able to configure and reconfigure itself dynamically as needs dictate.
It must constantly search for ways to optimize performance.
It must perform self-healing by redistributing resources and reconfiguring itself to work around any dysfunctional elements.
It must be able to monitor security and protect itself from attack.
It must be able to recognize and adapt to the needs of coexisting systems within its environment.
It must work with shared technologies. Proprietary solutions are not compatible with autonomic computing ideology.
It must accomplish these goals seamlessly without intervention.
FUNCTIONAL CHARACTERISTICS OF AUTONOMIC COMPUTING
SELF-CONFIGURATION
SELF-HEALING
SELF-OPTIMIZATION
SELF-PROTECTION
SELF-CONFIGURATION
Adapt automatically to the
dynamically changing
environment
• Internal adaptation
– Add/remove new components
– configures itself on the fly
• External adaptation
-Systems configure themselves
into a global infrastructure
SELF-HEALING
• Discover, diagnose and react to
disruptions without disrupting
the service environment
• Fault components should be
– detected
– Isolated
– Fixed
– reintegrated
SELF-OPTIMIZATION
• Monitor and tune resources
automatically
– Support operating in
unpredictable environment
– Efficiently maximization of
resource utilization without
human intervention
• Dynamic resource allocation
and workload management.
– Resource: Storage, databases,
networks
– For example, Dynamic server clustering
SELF-PROTECTION
• Anticipate, detect, identify
and protect against
attacks from anywhere
– Defining and managing user
access to all computing
resources
– Protecting against
unauthorized resource
access, e.g. SSL
– Detecting intrusions and
Reporting as they occur
PMAC- An Example of AUTONOMIC COMPUTING
• Policy Management for Autonomic Computing (PMAC)
– An autonomic core technology published in 2005
• Purpose: Providing a Policy management infrastructure
– Automating what administrators do today
• Administrators follow written policies
• With autonomic, autonomic managers follow machine-readable policy
• Autonomic Manager – Selects policies, evaluates policies, and
provides decisions to the managed element in order to manage its
behavior
• Using Autonomic Computing Policy Language(ACPL) as common policy language
– ACPL contains 4 tuples: Scope, Condition, Business value, Decision
• Scope represents managed elements, Business value is the decision priority
• Decision can be Actions, Configuration Profiles and Results
APPLICATIONS
Solution installation and deployment technologies
Integrated Solutions Console
Problem determination
Autonomic management
Provisioning and orchestration
Complex analysis
Policy-based management
Heterogeneous workload management
Short-term I/T related benefits
Simplified user experience through a more responsive, real-time system.
Cost-savings - scale to use.
Scaled power, storage and costs that optimize usage across both hardware and software.
Full use of idle processing power, including home PC's, through networked system.
Natural language queries allow deeper and more accurate returns.
Seamless access to multiple file types. Open standards will allow users to pull data from all potential sources by re-formatting on the fly.
Stability. High availability. High security system. Fewer system or network errors due to self-healing
Long-term, Higher Order Benefits
Realize the vision of enablement by shifting available resources to higher-order 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. Distributed computing allows for more immediate sharing of information and processing power to use complex mathematics to solve problems
CONCLUSION
The goal of the IBM autonomic computing initiative is to make IT systems self managing. Self-managed systems can adapt to changing environments and react to error conditions very efficiently. This ability to respond quickly helps reduce application downtime, which, in turn, can help prevent catastrophic loss of revenue. This a describes how an autonomic system, based on autonomic computing technologies, can be used to diagnose an error condition in an IT system and provide corrective actions.