28-04-2011, 04:01 PM
L11-autonomic-computing.ppt (Size: 3.13 MB / Downloads: 139)
Trillions of heterogeneous computing devices connected to the Internet
Dream of Pervasive Computing …
or Nightmare!
Core of the Problem
Complexity
in systems themselves and in the operating environment
As systems become more interconnected and diverse, architects are less able to anticipate and design interactions among components
push to runtime, late binding
e.g., hot-plug, JVM, JIT compilation, service discovery, mobile agents,
Complexity management
human intervention and IT costs
Need Complexity Management
But complexity is beyond that human can handle
Human out of the control loop autonomic
Even though we are moving along this direction, is there any systematic way of addressing this issue?
Autonomic Computing
Autonomic Computing
Complex Heterogeneous Infrastructures Are a Reality!
Industry Trends
Administration of systems is increasingly difficult
100s of configuration, tuning parameters for DB2
Heterogeneous systems are increasingly connected
Integration becoming ever more difficult
Architects can't plan interactions among components
Increasingly dynamic; frequently with unanticipated components
More burden must be assumed at run time
But human administrators can't assume the burden
6:1 cost ratio between storage admin and storage
40% outages due to operator error
Need self-managing computing systems
Behavior specified by sys admins via high-level policies
System and its components figure out how to carry out policies
Autonomic Computing Vision
“Intelligent” open systems that…
Manage complexity
“Know” themselves
Continuously tune themselves
Adapt to unpredictable conditions
Prevent and recover from failures
Provide a safe environment
Self-management:
free administrators from details of operations
provide peak performance 24/7
Concentrate on high-level decisions and policies
Self-managing Systems That …
Self-Configuring Example:DB2 Configuration Advisor
Self-Healing Example: IBM Electronic Service Agent
Self Optimizing:Enterprise Workload Management
Self-Protecting Example: IBM Tivoli Risk Manager
Evolving towards Self-management
IBM’s Architecture Model
Intelligent control loop:
Implementing self-managing attributes involves an intelligent control loop
Control Loops Delivered in 2 Ways
3 Layers of Control Loop Management
Composite resources tied to business decision-making
Composite resources decision-making, e.g., cluster servers
Resource elements managing themselves
Autonomic Element - Structure
Fundamental atom of the architecture
Managed element(s)
Database, storage
Autonomic manager
Responsible for:
Providing its service
Managing ownbehavior inaccordance withpolicies
Interacting with other autonomic elements
Autonomic Manager Substructure
Autonomic Elements - Interaction
Relationships
Dynamic, ephemeral
Formed by agreement
May be negotiated
Full spectrum
Peer-to-peer
Hierarchical
Subject to policies
Multiple Contexts for Autonomic Behavior
Mapping to IT Processes
Levels of Maturity
Autonomic Computing Requires Core Technologies
Integrated Solutions Console for Common System Administration
Value:
One consistent interface across product portfolio
Common runtime infrastructure and development tools basedon industry standards, component reuse
Provides a presentation framework for other autonomic core technologies
Log and Trace Tool for Problem Determination
Value:
Introduces standard interfaces and formats for logging and tracing
Central point of interaction with multiple data sources
Correlated views of data
Reduced time spent in problem analysis
Install/Config Package for Solution Install
Value:
One consistent software installation technology across all products
Consistent and up-to-date configuration and dependency data, key to building self-configuring autonomic systems
Reduced deployment time with less errors
Reduced software maintenance time, improved analysis of failed system components
Component-based install for IBM and non-IBM products
Policy Tools for Policy-based Management
Value:
Uniform cross-product policy definition and management infrastructure, needed for delivering system-wide self-management capabilities
Simplifies management of multiple products; reduced TCO
Easier to dynamically change configuration in on-demand environment
Technologies for Implementing Autonomic Managers
Value:
Components to simplify the incorporation of autonomic functions into applications
Building blocks for self-management
Monitoring, analysis, planning and execution components
Including autonomic computing technologies, grid tools, and services
Pluggable
Defines interfaces and provides implementations for each major toolkit component
Summary of Autonomic Computing Architecture
Based on a distributed, service-oriented architectural approach, e.g., OGSA
Every component provides or consumes services
Policy-based management
Autonomic elements
Make every component resilient, robust, self-managing
Behavior is specified and driven by policies
Relationships between autonomic elements
Based on agreements established and maintained by autonomic elements
Governed by policies
Give rise to resiliency, robustness, self-management of system
Summary