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The Role of Predictive Methods in Autonomic Computing April 27, 2005. Ric Telford Director of Architecture and Development, Autonomic Computing. Agenda. Autonomic Computing overview AC Problem Determination Technologies Customer Results The Self-Healing Vision Summary.

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The Role of Predictive Methods in Autonomic Computing April 27, 2005

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The role of predictive methods in autonomic computing april 27 2005 l.jpg

The Role of Predictive Methodsin Autonomic ComputingApril 27, 2005

Ric Telford

Director of Architecture and Development, Autonomic Computing


Agenda l.jpg

Agenda

  • Autonomic Computing overview

  • AC Problem Determination Technologies

  • Customer Results

  • The Self-Healing Vision

  • Summary


Today s complex infrastructure l.jpg

Today’s Complex Infrastructure

IT asset utilisation is too low

Management of complex,

heterogeneous environments

is too difficult

Privacy, security and

business continuity

WWW

Swamped by the proliferation

of technology and platforms

to support

Operational speed too slow;IT flexibility too limited

Inability to manage the

infrastructure seamlessly


Focus on business value not infrastructure l.jpg

Focus on business value, not infrastructure

Autonomic Computing delivers intelligent open systems that:

  • Adapt to unpredictable conditions

  • Continuously tune themselves

  • Prevent and recover from failures

  • Provide a safe environment

Sense and respond to ever-changing environments

Providing customer value

“IBM’s autonomic computing initiative will become its most important cross-product initiative (as the foundation of On Demand Business).”

— Thomas Bittman, Gartner

  • Increased return on IT investment

  • Improved flexibility, resiliency and quality of service

  • Accelerated time to value


Ibm autonomic computing structure l.jpg

IBM Autonomic Computing Structure

  • Autonomic Computing Control Loop

  • Autonomic Computing Architecture Blueprint

Autonomic Computing Architecture

Products delivering

autonomic features

  • 50 products with 415+ features

  • Partner solutions

  • Log/Trace Analyzer

  • Generic Log Adapter

  • Solution installation & dependency checking

  • Common Console

  • Autonomic Management Engine

Management Engine

Installation

Autonomic Computing Common Components

Problem Determination

Provisioning

Workload Mgt

Admin Console

Open Standards

  • Common log format

  • Solution installation schema


Autonomic computing problem determination technologies l.jpg

Autonomic Computing:Problem DeterminationTechnologies


The pain point l.jpg

Fire

Wall

Fire

Wall

Load

Balancers

Network Routers/Switches

Fire

Wall

Load

Balancers

Edge Servers

Security Servers

Load

Balancers

HTTP

Servers

Data

Servers

Application

Servers

Managing Servers

LDAP Registries

Backup Servers

Fire

Wall

You

Policy Servers

The Pain Point….


Today s approach internal swat team the manual process l.jpg

Today’s Approach… Internal Swat Team– The Manual Process

  • Requires:

    • Key resources across the IT staff to get the breadth of skills to understand the end-to-end problem

    • Deep understanding of log file formats

    • Deep understanding of system components

Blame Storming

  • Result:

    • Multiple man-hours/days/weeks of effort

    • Political issues – passing the blame

    • Insufficient / inadequate data can cause this approach to fail

  • Customers are repeating this step today for every major IT outage


Log format today l.jpg

Common Base Eventan OASIS standard

Applications

Adapters

Adapters

Database

  • Disparate pieces and parts

  • Tools focused on individual products

  • No common interfaces among tools

  • No synergies in building tools OR in creating log entries

common base event

ApplicationServer

Servers

  • Generic log adapter

  • Common format for log files

  • Common set of tools

  • Common interfaces among tools

Storage devices

Networks

Problem determination: Log format tomorrow

Log format today


Common base event format l.jpg

Common Base Event Format


Supported log formats feb 2005 l.jpg

AIX errpt log

AIX syslog

Apache HTTP Server access log

Apache HTTP Server error log

CICS Transaction Server for z/OS System message log

Common Base Event XML log

ESS (Shark) Problem log

IBM Communications Server log

IBM DB2 Express diagnostic log

IBM DB2 Universal Database Cli Trace log

IBM DB2 Universal Database JDBC trace log

IBM DB2 Universal Database SVC Dump on z/OS

IBM DB2 Universal Database Trace log

IBM DB2 Universal Database diagnostic log

IBM HTTP Server access log

IBM HTTP Server error log

IBM WebSphere Application Server activity log

IBM WebSphere Application Server for z/OS error log

IBM WebSphere Application Server plugin log

IBM WebSphere Application Server trace log

IBM WebSphere Commerce Server ecmsg log

IBM WebSphere Commerce Server ecmsg, stdout, stderr log

IBM WebSphere InterChange Server log

IBM WebSphere MQ FDC log

IBM WebSphere MQ error log

IBM WebSphere MQ for z/OS Joblog

IBM WebSphere Portal Server appserver_err log

IBM WebSphere Portal Server appserverout log

IBM WebSphere Portal Server run-time information log

IBM WebSphere Portal Server systemerr log

IBM WebSphere Portal Server systemout log

IBM Websphere Edge Server log

Javacore log

Logging Utilities XML log

Microsoft Windows Application log

Microsoft Windows Security log

Microsoft Windows System log

Oracle JDBC trace log

Oracle alert log

Oracle listener log

Oracle server log

Rational TestManager log

RedHat syslog

SAN File System log

SAN Volume Controller error log

SAP system log

Squadrons-S Problem log

SunOS syslog

SunOS vold log

TXSeries CICS Console/CSMT log

z/OS Component trace

z/OS GTF trace

z/OS Joblog

z/OS Logrec

z/OS System log(SYSLOG)

z/OS System trace

z/OS master trace

Supported Log Formats (Feb 2005)


Log correlation generating the end to end view l.jpg

Log Correlation – Generating the End-to-End View

  • With Correlation IDs in place, or Correlation methods identified:

    • Implement a Correlation Engine in the Log Analyzer

    • Generate a sequence diagram showing the log interactions and sequence of events

  • Help the IT staff hone in on where the problem occurred:

    • Identify quickly where to concentrate efforts

  • Transition from trying to understand log formats to identifying ways to analyze the overall data and the end-to-end view

  • Move the Mindset from Monitoring to Analysis


End results l.jpg

End Results…

From

To

Single PD-Skilled Resource

Multiple IT-Skilled Resources

Multiple Man-Hours / Days / Weeks of analysis

Root Cause identification in hours / minutes

Unstructured Swat Team Approach with success unknown

Repeatable Process with a reusable set of tools


Self healing customer results l.jpg

Self-Healing - Customer Results

From several hours/days to less than one hour

60% Improvement

85% Improvement

50% Improvement – IBM’s SAP Deployment

70% Improvement

60% Improvement

50% Improvement

10 to 30% Savings in IT Support Costs

20 to 30% Improvement

10 to 20% improvement in operational staff productivity – IBM Software Delivery and Fulfillment

75% Improvement

From 3 people 2 hours to 1 person 15 min

40% Improvement

New in 2005


Self healing roadmap l.jpg

Self Healing

Remediation

Self-Healing Roadmap

Business Policy

Continuous Availability

Knowledge Sharing

Action Representation

Knowledge Accumulation

Customer Pull

Analysis

Knowledge Representation

Event Correlation and Analysis

Partner Deployers

2007

Capture

Event Representation

Adapters

IBM Deployers

2006

  • Business policies guide self-healing system

  • Preemptive diagnostics automatically recognize and resolve problems

  • Call home facilities are integrated as part of self-healing solutions

  • Symptom data made available to customers, ISVs, partners

  • Standardize data model for change requests, change plans

  • Standardize grammar to describe change requests and constraints

  • Allow analysis and planning when uncertainty is present

  • Allow human to determine recovery action

  • High-profile customer deployments and references

2004-2005

  • Standardize data model for symptom analysis

  • Transport & correlate events from all components in IT infrastructure

  • Predictive Analysis Constructs

  • ARM Correlation

2004

  • Standard data model for common situation and event reporting

  • Tooling for easy adoption of standard

  • Commitments from IBM brands and IBM Partners to support the data model


Self healing vision l.jpg

P

P

P

P

A

A

A

A

E

E

E

E

M

M

M

M

Call

Home

Sensor

Effector

Human-based MAs

and associated

tooling for correlation,

analysis, viewing

Change Type

Plan

Analyze

Change Plan

Symptom

Symptom

Tooling

Knowledge

Policy

Execute

Monitor

IT Professionals

Config

CBE

Action

Win

SS

AIX

DB2

MQ

zOS

DB2

MQ

Adapter

Increased Embedded Self-Management Function

Self-Healing Vision

CBEs


Summary l.jpg

Summary

  • IBM’s Autonomic Computing initiative has helped deliver the right “hygiene” to enable the industry for better Problem Determination

  • Predictive technologies can capitalize on this hygiene to help automate the “Problem Determination” process

  • We need continued research and cooperation across IBM and the industry at large to make the vision of Self-Healing systems a reality!


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