Case Study: Vivo Automated IT Capacity Management to Optimize Usage of its Critical Infrastructure Environment

Case Study: Vivo Automated IT Capacity Management to Optimize Usage of its Critical Infrastructure Environment Henio Aires Almeida

OCX58S @henioaires #CAWorld

ca Opscenter

Telefonica Vivo Telecom IT Capacity Manager

2 © 2014 CA. ALL RIGHTS RESERVED.

Abstract

The main IT challenges in business today are to support new business

offers and the fast implementation of new applications that the

business requires. Vivo uses CA Capacity Management to monitor

current capacity and assure the optimized usage of their critical

infrastructure environments. This enables them to dispose of manual

procedures and spreadsheets and reveals increased time to value and

high speed to deliver, accelerating the resolution to business requests

and helping the company to shorten strategic application and business

initiatives’ time to market. The solution also allows Vivo to stay in

compliance with local regulatory agencies. (See related session: Meet

The Experts: Q&A for “Vivo Automated IT Capacity Management to

Optimize Usage of its Critical Infrastructure Environment.")

Henio Aires Almeida

Telefonica Vivo Telecom IT Capacity Manager


Agenda

COMPANY INTRODUCTION

CHALLENGES BEFORE/DURING DEPLOYMENT

SUCCESS/BENEFITS AFTER DEPLOYMENT

OUR IMPLEMENTED CAPMAN SOLUTION/GENERAL RECOMMENDATIONS

OUR NEXT STEPS – WHERE WE’RE GOING & EXPECTED SUCCESSES

1

2

3

4

5


Company Introduction

Telefónica (Brazil Accesses June 2014)

Fixed Telephony: 10,929.1

Mobile: 79,350.7

Telefónica is one of the largest telecommunications

companies in the world in terms of market capitalization

and number of customers.

Present in 24 countries and a customer base that

amounts more than 315.7 million accesses around the

world.

At the end of 2013 Telefónica's revenue in Brazil reached

12,217 million euros. Brazil is now firmly established as

Telefónica's largest market in the region and it generates

almost 42% of the revenue of Telefónica in the region.

Our Brazilian customers know us as Vivo

Internet & Data: 4,103.5

Pay TV: 687.8

Total Accesses: 95,098.1

(Data in thousands accesses)


Vivo and CA On The Journey To Capacity Planning Maturity

We are here!

The process requires a lot of

operational effort

We maintain a process to identify and address performance improvements in our IT environments

We have a formal annual capacity plan with regular monitoring reports of environment

We are part of the management process and requests for impact analysis and demands on infrastructure

We have a mature process management ability and structure, with dedicated staff


About our process automation for capacity manager

Level 1 • Manual processes

Level 2 • Reports

automated and centralized capacity

Level 3 • "What-if” scenario

modeling (manual)

• Correlate with volumetric business and environments consumption (manual)

Level 4 • "What-if“ scenario

modeling (automatic)

• Correlate with volumetric business and environments consumption (automatic)

Level 5 • Proactive • Transactional

applications • Predictive

analytics for optimal sizing & performance

Automation

Although our management process capability is mature, our automation was low and the process of

analysis depended on operational effort to transform the raw data collected for analysis.

Automation levels

LOW HIGH

We are here!


Our Challenges

Raise the level of automation at the same level of process maturity, but how?

Implement a specialized tool to accelerate the process and be able to:

– Reduce manual effort applied to current capacity planning allowing the scope of new

systems as well as increase participation in the analysis of business demands

– Rapid identification of changes in the behavior of systems allowing preventive actions to

mitigate impacts on environments and consequently on business

– Elaborate ‘what-if’ scenarios projections correlating transactions, infrastructure and

business data


Our Deployment Challenges

CURRENT CAPACITY REPORTER (CCR)

CAPACITY COMMAND CENTER

(CCC)

CA

Cap

acit

y M

anag

em

en

t M

od

ule

s

DATA MANAGER (DM)

ADMINISTRATION

CAPACITY DATABASE

DATA MANAGER (DM)

DATA MANAGER DATA GATHERING ENGINE

DATA ADAPTER

DATA ADAPTER

DATA ADAPTER

DATA ADAPTER

DATA ADAPTER

DATA ADAPTER

DATA ADAPTER

access.log PARSER

PERFORMANCE OPTIMIZER (PO)

SPREAD SHEETS

SER

VIC

E O

PER

ATI

ON

APLICATION MANAGEMENT

VCENTER NMON SAR AUTOMATION

INFRASTRUCTURE MANAGEMENT

SERVICE DEFINITIONS

CAPACITY MANAGER (CAPMAN)

STAGING AREA HP

SITESCOPE

Staging elaborate structure to normalize and transform the data intended for

custom reports

Challenge: Replacing the basic data collection infrastructure tool during the

project

Development of specific scripts to collect capacity metrics


CA Capacity Management Created Structure

CA Capacity

Management

New infrastructure for Control-M and SiteScope servers to support the entire data collection consumed and processed by CA Capacity Management.

Number of Collections:


Successes/Benefits Measured

Description of the effective gain Before After Unit Effective gain

Effort for creating technical periodical reports 410 10 Min 97%

Effort for data collection to fill out the Capacity Panel (Executive Weekly Report)

80 7 Min 91%

Time invested in impact analysis for business demands 64 96 hour/month 50%

Number of servers covered by the capacity team 1.143 1.500 Amount 31%

Number of environments covered by the capacity team 84 93 Amount 11%


Our Implementation

Data Manager is the performance data repository for our capacity analysis

In addition, responsible for environment monitoring report generation

Data Manager (DM)


Executive Views Capacity Command Center (CCC)

Distribution of capacity available for server groups supporting critical applications

Drill down to individual server utilization metrics


Standard Reporting Current Capacity Reporter (CCR)

CCR display capacity data in various dashbaord formats

VMware cluster CPU trends

CPU Heatmap

Usage graph & projected CPU consumption


What are we doing now?

Capacity Manager will predict infrastructure performance against workloads

• Transferring our predictions from manual spreadsheets to CA Capacity Management • Currently working with simple projections, no automatic correlation between

transactions and infrastructure


Our Next Steps

Performance Optimizer (PO) predicts application performance, response times and infrastrcuture utilization resulting in an end-to-end service view

• Preparation of complex projections with advanced "what-if“ scenarios, and create models of correlation between the transaction, infrastructure and business data


General Recommendations

Before implementing the tool

– create a formal process for capacity analysis and implement the tool to support this

process with a dedicated team

– have a structured method for data collection

Start with critical environments first

Take part of the management demand process for impact analysis on infrastructure

Make periodic analysis of current consumption vs projections

Have a process for dealing with identified deviations


For More Information

To learn more about DevOps, please visit:

http://bit.ly/1wbjjqX

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ensure it links to correct page DevOps






For Informational Purposes Only

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Case Study: Vivo Automated IT Capacity Management to Optimize Usage of its Critical Infrastructure Environment