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Autonomic Computing
© 2004 IBM Corporation
Autonomic Computing in Action
Ric TelfordDirector, Strategy and TechnologyAutonomic ComputingApril 14, 2004
2
Autonomic Computing
© 2004 IBM Corporation
Agenda
�Putting the pieces together
�Autonomic Computing system examples
�Building a self-healing system
�A self-configuring system
�Autonomic Computing research
�Self-configuring devices
�Self-optimizing web servers
�Utility functions
�Autonomic Computing Storage
�Ethnographic Studies
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Autonomic Computing
© 2004 IBM Corporation
AMKS
TP
Registries
Knowledge Services
Putting the pieces together
EM
A P
K
Symptom
Action Plan
CBEs
MR
MR
CommandsLogs
Configuration Files
EventsAPIs
Sensor Effector
MR
MR
CommandsLogs
Configuration Files
EventsAPIs
Sensor Effector
EM
A P
K
Symptom
Action Plan
CBEs
When autonomic
managers are introduced
The AMs must register
with the fabric.
This knowledge must be
passed to the ‘fabric’. AND
the touchpoints must
register themselves with the
fabric.
Now the system is ready to
run! CBEs can flow, MAPE
loops can make decisions,
resources can be managed
CBEsCBEs
Execution Workflow Execution Workflow
Initially, there is the ‘fabric’.
The ‘services’ in the fabric
must register themselves
with the Fabric Registries
Managed resource prototype
Policies
Symptoms
Treatable cause
Configuration
Dependency Data
Managed resource prototype
Policies
Symptoms
Treatable cause
Configuration
Dependency Data
} {
When managed resources
are introduced
There is knowledge that is
created about each
managed resource.
Knowledge Creation
Knowledge about MRs,
symptoms, etc. must
either be passed to the
AMs
OR it must be made
available via k-services
�Symptom: something
that indicates the
existence of something
else (from
webster.com)
AMs and TPs must talk to
fabric registries to find the
appropriate
manager/touchpointES ES
CBEs are created by MRs
CBEs are passed to the fabric,
which uses the deliver-
notification interaction style to
deliver CBEs to AMs
The ‘M’ function correlates CBEs
into Symptoms
Symptoms are passed to ‘A’
‘A’ creates an action and passes
that to ‘P’
‘P ’creates a plan and passes that
to ‘E’
‘E’ executes on the plan, using the
fabric and the perform operation
interaction style
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Autonomic Computing
© 2004 IBM Corporation
� Disparate pieces and parts
� Tools focused on individual products
� No common interfaces
among tools
� No synergies in building tools OR in creating log entries
Problem Determination todayProblem Determination tomorrow
� Generic log adapter
� Common format for
log files
� Common set of tools
� Common interfaces
among tools
common base event
Ad
ap
ters
Ad
ap
ters
Common Base Eventsubmitted to OASIS
Common Base Eventsubmitted to OASIS
Database
Networks
ApplicationServer
Servers
Storage devices
Applications
5
Autonomic Computing
© 2004 IBM Corporation
Ad
ap
ters
Autonomic computing self-healing systems
Feedback
Data
Polic
ies
Autonomic
manager
Knowledge
Policy
engine
Symptom
ApplicationServer
Servers
Storage devices
Database
Networks
Applications
Ad
ap
ters
Common Base Event
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Autonomic Computing
© 2004 IBM Corporation
IBM Global Services
©2003 IBM Corporation
In a basic problem bypass and resolution service flow, most tasks and information exchanges are performed manually
Customer
External Service
Problem Manager
Problem Analyst
Problem Queue Manager
Automated Tools
Customer-Detected Problem
Resolved Problem
Report Problem Status and Trends
Incident Management
Incident Management
Change Management
Configuration Management
Manage Problem
Resolution
Bypass Problem
Analyze Problem
Identify Problem
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Autonomic Computing
© 2004 IBM Corporation
EM
A P
K
Symptom
Action Plan
Self-healing Vision
SymptomsSymptoms
Managed
Resource
Properties
Symptom 1
Component: WAS
Situation: DEPENDENCY NOT MET
Correlation: MATCH
Treatable Cause: Path configured wrong
Symptom: Symptom 1
Action : CONFIG PATH
Identify
Problem
Manage
Problem
Resolution
Analyze
Problem
In order for AM to operate:
- Knowledge must be created about the
symptoms and treatable causes.
- Knowledge about the MR, like what sensors and
effectors it has must be created
- Policies must be created based on customer
goals
- All this knowledge must be passed into the AM
Managed Resource : WAS
Sensors Implemented:
SendEvent
Effectors Implemented
CheckPath
ConfigurePath
CBEs Generated
Dependency
…..
Knowledge CreationManaged
Resource
Prototype
Scope: WAS Server
Condition:
8am to 6PM
BV: 100
GOAL: 99.5 % Availability
PoliciesPolicies
When an error occurs:
Monitor takes in CBEs
Monitor uses correlation technology to identify Symptoms
Monitor passes symptoms to Analyze
Analyze uses Decision Tree (or symptom service) to determine Treatable Cause
Analyze determines best corrective action and passes that to Plan
Plan creates a plan to enact
Execute takes the actions. ES
CBEs
MR
MR
CommandsLogsConfiguration Files
EventsAPIs
Sensor Effector
CBEsComponent: WAS
Situation: DEPENDENCY NOT MET
Data: EAR file missing
Check Path
Configure ear Path
Autonomic Manager
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Autonomic Computing
© 2004 IBM Corporation
IBM Global Services
©2003 IBM Corporation
In a more autonomic workflow, the infrastructure can detect and bypass many problems and the tools help automate the informationexchange between tasks
Customer
External Service
Problem Manager
Problem Analyst
Problem Queue Manager
Automated Tools
Problem Management
Reports
Identify Problem
Report Problem Status and
Trends
Event Management
Incident Management
Change Management
Configuration Management
Manage Problem
Resolution
Bypass Problem
Analyze Problem
Event Mgmt Tool
Config. Mgmt Tool
Change Mgmt Tool
Incident Mgmt Tool
Problem Mgmt Tool
Incident Management
Problem Mgmt Tool
Incident Mgmt Tool
Event Mgmt Tool
10
Autonomic Computing
© 2004 IBM Corporation
AutonomicPCs
Autonomic Storage
AutonomicDatabase
Technology
AutonomicMiddleware
Autonomic Capabilities for eServer
Focus on Autonomic Computing Research
Autonomic Computing
Core ResearchAC building block
technologies
Policy technologies
System prototyping
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Autonomic Computing
© 2004 IBM Corporation
Autonomic Computing ResearchPCE - Personal Software Configuration Engine
inventorycollection
Analysis
rules
Analysis:characterize
inventory
PPE
Plan:Choose
components, resolve
dependencies
Smart
Catalog
Clean up?
New software?Make space?
install/uninstall
Planning
rules
Policies
ACTI ACTI ‘‘0202
� Automate SW
maintenance &
migration on personal devices
� “Upgrade all my
applications”
� “Make my new laptop
work like the old one”
� “Migrate most valuable
Palm apps to my PC”
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Autonomic Computing
© 2004 IBM Corporation
Fewminutes
later…
An important scenario: Workload surge
�� SystemsSystems cancan go go fromfrom steadysteady state state ……
Internet
�� to to overloadedoverloaded withoutwithout
warning warning
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Autonomic Computing
© 2004 IBM Corporation
Autonomic Computing: Dynamic SurgeProtection
DatabaseWeb
Application
Servers
Driver (simulates
Internet in/out)
SurgeButton
MonitoringConfiguration management
Sensor Effector
ControllerForecaster
Performance Modeler
Statistics /Knowledge
Element
Monitor
Analyze
(Forecaster)
Sensors
Execute
(Controller)
Plan
(Perf Modeler)
Effectors
Knowledge
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Autonomic Computing
© 2004 IBM Corporation
Response Time
Actual BOPS
Predicted BOPS
#Active Servers
#Requested Servers
1
1. Steady State
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Autonomic Computing
© 2004 IBM Corporation
2. Monitor, Detect Surge
Response Time
Actual BOPS
Predicted BOPS
#Active Servers
#Requested Servers
1 2
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Autonomic Computing
© 2004 IBM Corporation
3. Forecast, Provision Servers
Response Time
Actual BOPS
Predicted BOPS
#Active Servers
#Requested Servers
1 21 2 3
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Autonomic Computing
© 2004 IBM Corporation
Response Time
Actual BOPS
Predicted BOPS
#Active Servers
#Requested Servers
4. Monitor, Remove Servers1 2 43
18
Autonomic Computing
© 2004 IBM Corporation
Utility Functions and Autonomic Computing
� Utility functions can guide autonomic
decision making
� Self-optimization: natural way to
express optimization criteria
•Declarative: preferable to implicitly hard-
coded in special purpose algorithms
� Derivable from business objectives
(e.g. optimize total profits)
•Can translate to computing metrics at
different levels
� Exploring applications in Workload
Management, other areas
Response time RT
V(R
T)Utility function
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Autonomic Computing
© 2004 IBM Corporation
IBM IceCube Server
“Brick”
10 Gbit/s
capacitive
“Coupler”
(6) per brick
=
“Thermal
Bus Array”
6”
Prototype Brick:
- (12) 2.5” disks
- 8-port Switch
- Linux on fast CPU
Full IceCube System
blue: Storage Bricks
yellow: Compute Bricks
3D mesh @ 10 Gb/s per link
No connectors,
wires, fibers,
lasers or fans
� Lego-like Collection of ‘Intelligent Bricks”
� Fail-in-place policy: bad bricks are left in place
� 7 x smaller than equivalent standard systems
� Fast, power-hungry components (CPU etc) ok
� Includes resource allocation software
� First Application : Petabyte-class Storage Server
� intended to be managed by one person
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Autonomic Computing
© 2004 IBM Corporation
Ethnographic Field Studies of Middleware Admins
DB2
Poughkeepsie, IBM IGS SDC
WAS
Boulder, IBMIGS SDC
WAS/Middleware
Southbury, IBMIGS SDC
WAS/Middleware
Southbury, IBM IGS SDC
� Early finding: Collaboration compensating for system complexity and simple tools
DB2
Charlotte, Belk
Video Log Spreadsheets
� Study and Analysis methods used to study Sys Administrators at work
“Let’s do it together”
Task Time ChartsVideotape Logger Video Analysis Lab
� What tasks do Autonomic Systems need to simplify for Sys Administrators?
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Autonomic Computing
© 2004 IBM Corporation
What do those admins do? (Video Analysis)
face to face
5%
4%
command line
11%
web
3%
phone
36%
instant
messenger
16%
gui
21%
other
4%
face to face
22%
2%
command line
6%
web
3%
phone
44% instant
messenger
23%
Tool Usage Topics Discussed
• ~90% time collaborating
• Main topics • collaboration
• configuration• strategies
• ~60% time collaborating
• Main topics
• commands• strategies• collaboration
• configuration
log
0%
personal
2%
strategy
20%
error
3%
documentation
8%
administrative
3%
collaboration
22%
command
24%
configuration
17%
state
1%
log
3%
personal
3%
strategy
15%
state
11%
tools
2%
configuration
31%
command
7%
collaboration
21%
administrative
0%
documentation
3%
error
4%Tro
ub
lesh
oo
tin
g (
2.5
ho
urs
)N
ew
Web
Sealin
sta
nce
Tro
ub
lesh
oo
tin
g (
2 h
ou
rs)
IIS
Cert
ific
ate
issu
e
22
Autonomic Computing
© 2004 IBM Corporation
Preliminary findings for AC Console UI designs
• Troubleshooting depends on information access
• Need clear and consistent naming conventions for system objects
• Support easy correlation of log data and config settings
• Make logs browsable, visualizable, query-able, annotatable
• Place events in the context of end-to-end systems rather than of components
• Admins build their own tools, configure their own environments
• Support “tools built by admins for admins”
• Provide composable components and appropriate combination methods
• Enable widespread sharing of admin developed tools
• Collaboration is a vital part of admin practice
• Help establish trust among participants
• Support side-by-side exploration of system events,
configurations, etc.
• Enable ready, shared access to all relevant information
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Autonomic Computing
© 2004 IBM Corporation
Summary
� Autonomic Computing architecture
components and interfaces come together
to form a system
� Today’s IT processes can be improved
through Autonomic Computing� IBM Research is working on some of the
hard problems in Autonomic Computing, but
there is still a lot of work to do!
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Autonomic Computing
© 2004 IBM Corporation
© Copyright IBM Corporation 2004. All rights reserved.
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