autopilot it operating

EUROPEAN TIVOLI TECHNICAL CONFERENCE 2010IBM

Presentation Title: Building Autopilot IT Operation on a Tivoli Platform

Speaker: Hans-Christian Boos

Designation: CEO arago AG

Session Number: SDPA21


THE BUSINESSCASE OF AUTOMATION


What Is Success ?

Money + Talent = Innovation

Talent * Innovation = Success

So, We Are Looking for Talent and Money!


What Is Talent?

Talent = YOU !

But Wait, YOU Are BUSY Keeping Things Going.

NO TIME FOR INNOVATION !


Why Automation Part I

• To Get the Boring Things You Have All Done 1.000 Times off Your Backs....

• To Learn, to Build, to INNOVATE


So Where Is the Money Going ?

The Number of IT Systems and Thus the Dependency on IT Systems Grows Constantly

Num

ber

of I

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ns

Time


Where Is the Money Going?

Operating Cost of IT Has Grown Exponentially. (Today 53% of Cost is Infrastructure)

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StaffInfrastructure



Fortunately, IT Budgets Are Capped !

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ber

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StaffInfrastructure

You Are Here

IT B

udge

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Cloud Computing Was Reinvented to Fix It All….

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But Not Everything Can Go into the Cloud and Clouds Have Operational Overhead (No Free Lunch, Sorry)

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You Are Here

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“Cloud Staff”Cloud Infrastructure


Why Automation Part II

Automation Helps to Get “Things” into theCloud, Reduces Standard Operational Expense, and Reduces Cloud Overhead!

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AUTOMATION CONCEPTS


Automation Classic Style

Today We Believe That Automation Means Creating “a Script” In One Way or the Other

Start End

Application Operation

Plattform/OSInfrastructure Apps


In The End It’s All About Knowledge !

To Perform a Task All You Normally Do Is Recombine What You Already Know

Knowledge Repository

Application Operation

Infrastructure Apps

Platforms/OS


An Autopilot System

Our Autopilot Will Evaluate a Situation Step-By-Step Recombining Available Knowledge To Fulfill the Task at Hand

Knowledge Repository

Start

Analysis

Decision

Analysis

Decision

Analysis

Decision

EndAnalysis


Do Something Creative !

16 |

AutomatedCorrelation

AutomatedEvent-Mgmt.

AutomatedITSM Processes

AutomatedDiscovery

AutomatedRoot-Cause-Analysis

AutomatedProvisioning

WorkloadAutomation

AutomatedConfig. Mgmt

Data-CenterAutomation

AutomatedRun-Books

ComplianceReporting

AutomatedService Desk

AutoPilot


Strategic Importance

Ope

ratio

nal I

mpo

rta

nce

80 % Run

20 % Change

Strategic Importance

Ope

ratio

nal I

mpo

rta

nce

40% Run

60% Change

Finally Getting the Time and Funding to

Change the Things that Need Changing.

Moving From Run to Change!Status Quo After (Automation + Cloud)


TECHNICALLY BUILDING AUTOPILOT ON TIVOLI PLATFORMS


Autopilot Behaves Like a Colleague

100% Process Integration from the Start


PRE REQUIREMENT

PRE REQUIREMENT RESULTSRESULTSAUTOPILOTAUTOPILOT

Basic Autopilot I/O Model

Good Monitoring and an IT Model Are

The Foundation the Autopilot Is Built upon

arago AutopilotEngine


KnowledgeKnowledge

IT ModelIT Model

MonitoringMonitoring

ActionsActions

DocumenttionDocumenttion


In a Basic Tivoli Environment

Model from TADDM / CCMDB

Monitoring from ITM, Historic Data from DWHS and Action Execution by ITM Agents – All Via Omnibus

IT ModelIT Model Monitoring & AgentsMonitoring & AgentsAUTOPILOTAUTOPILOT


arago AutopilotEngineTADDMTADDM

Om

nibu

sO

mni

bus

Tivoli DWHSTivoli

DWHS

ITM Agents

ITM Agents


In an Advanced Tivoli Environment

Tivoli Impact /NetCool

GW

Business Service

Management (TBSM)

Configuration Database (CCMDB) O

MN

Ibu

s

Ressource Monitoring

(ITM)

Tivoli Data Warehouse

Transaction Monitoring (ITCAM)

Network Monitoring

(TNM)

3rd Party Data

Source

OtherRepositories

metrics (real time)

automation

events

events / KPIs

Config Items and Relationships

ARAGOAUTOPILOT

ENGINE

metrics (historical)

aAE


The M-ARS Way of IT Modelling

Machine

Application

Ressource

Software

M

M

A

A

R

R

S

S

R

R

S

S

M

M

R

R

S

SS

S

= a machine or other technical component, which forms part of the IT Infrastructure. It always delivers technical data of itself and of the links or software components running on it.

= Resources are combined to applications using a defined order, to reflect functional dependencies of an application for the user or related to the adherence to delivery dates.

= Services are grouped into resources, to display 1-to-1 dependencies (e.g. does a SAP-Service need a functioning data base service).

= Several machines jointly delivery a specific service to an external party, i.e. a user or different applications (e.g. a cluster of Web Servers offering an identical portal as service)


CCMDB/TADDM to aAE

Model Export With Filter and Custom Data Structures.

Model Feedback from aAE

CC

MD

B /

TA

DD

MC

CM

DB

/ T

AD

DM

CU

RR

EN

TC

UR

RE

NT

AC

TU

AL

AC

TU

AL

Export to XML

Export to XML FILTERFILTER MARS

ToolsMARSTools

M-A

RS

M-A

RS

MARSlint

MARSlint

aAE

aAE

CustomModel

CustomModel

CustomData

CustomData


Omnibus to aAE for Monitoring

Straight Forward Native Interface

OmnibusOmnibus

Native Tivoli InterfaceNative Tivoli Interface

FilterFilter

NormalizationNormalization

aAE Data BusaAE Data Bus

Monitoring Automation Interface DescriptionMonitoring Automation Interface Description


Monitoring <-> Model Connection

MAID (Monitoring to Automation Interface Description) Subscribes Data and Events to Model Nodes

Om

nibu

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bus

CCMDB / TADDMCCMDB / TADDM

M-ARS ModelM-ARS Model

aAE Stream

aAE Stream

MA

IDM

onito

ring

to A

utom

atio

n In

terf

ace

Des

crip

tion

MA

IDM

onito

ring

to A

utom

atio

n In

terf

ace

Des

crip

tion

Monitoring Data

Attached to Model Nodes

Monitoring Data

Attached to Model Nodes


aAE Execution via Tivoli

Execution Chains Are Built Through Write or Execute Events.

ssh and Other Protocols as Backup

aAE

aAE

Nat

iv I

nter

face

Nat

iv I

nter

face

Om

nibu

sO

mni

bus

Age

ntA

gent

Write FileWrite File

ExecuteExecute


Sizing

► Key Indicators for the arago Autopilot Engine ► Number of nodes in all models: 231.277► Number of nodes in largest model: 27.978► Number of machines in all models: 8.452► Amount of data processed per month: 102,7 GB

► Current aAE environment at arago:► 2 x Server Intel Dual Core 2,2 GHz, 4GB RAM, Red Hat 5.2► 1 x NAS 7,5 TB as Data Archive


Scalability

1 2 4 8 16 32 64 128 2560%

20%

40%

60%

80%

100%

120%

Konventionell Bei Einsatz aAE

Number of People in Production Team

Tim

e U

sed

fo

r P

rod

uct

ivel

y


KNOWLEDGE POOL AND INTERACTION


Knowledge Structure

By Separating Decision and Execution Knowledge Reuse Is Increased Even More

MAID as Monitoring InterfaceMAID as Monitoring Interface

Internal Layer (Self Controlling Machine)Internal Layer (Self Controlling Machine)

Decision KnowledgeDecision Knowledge

Execution KnowledgeExecution Knowledge


Knowledge Transfer Format


THANK YOU FOR YOUR TIME

Questions ? Please Ask

More Questions ? Please email me at [email protected]

Want Opinions ? Read more at www.hcboos.net

autopilot it operating

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