Capacity planning for the newer workloads
1 / 70

Capacity Planning for the Newer Workloads - PowerPoint PPT Presentation

  • Updated On :

Capacity Planning for the Newer Workloads. Linwood Merritt Capital One Services, Inc. Disclaimer. These generic issues are addressed by this presentation: Vendor capacity ratings e-Commerce Continuous availability Data warehousing Growth rates

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Capacity Planning for the Newer Workloads' - tahir

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Capacity planning for the newer workloads l.jpg

Capacity Planning for the Newer Workloads

Linwood Merritt

Capital One Services, Inc.

Disclaimer l.jpg

  • These generic issues are addressed by this presentation:

    • Vendor capacity ratings

    • e-Commerce

    • Continuous availability

    • Data warehousing

    • Growth rates

  • This presentation contains no specific business-related information.

Introduction environment l.jpg
Introduction: Environment

  • Capital One

    • 5th largest card issuer in the United States

    • Capital One to S&P 500 in 1998

    • Fortune 500 company (#260)

    • Managed loans at $48.6 billion as of Q1 2002

    • Accounts at 46.6 million as of Q1 2002

    • Fortune 100 “Best Places to Work in America”

    • CIO 100 Award “Master of the Customer Connection”

    • Information Week “Innovation 100” Award Winner

    • ComputerWorld “Top 100 places to work in IT”

Outline of approach l.jpg
Outline of Approach

  • Understand behavior and issues around workloads, hardware, and data

  • Create projections and build recommendations.

  • Report the findings.

Outline of presentation l.jpg
Outline of Presentation

  • Discussion of workload types and capacity projection approaches

  • Overall summary of issues and approaches

  • Examples

What workloads l.jpg
What Workloads?

  • E-Commerce

  • Relational database systems

  • Mainframe-class UNIX

  • Multiple platforms

  • New characteristics

E commerce workloads direct to client business to business l.jpg
e-Commerce WorkloadsDirect to Client (business-to-business)

  • Access

    • Internet

    • Leased line

  • Services

    • Point of Care / Point of Sale

    • Value-added analysis

E commerce workloads direct to customer l.jpg
e-Commerce WorkloadsDirect to Customer

  • Access

    • Internet

    • Dial-in

  • Services

    • Marketing

    • Account query

E commerce workloads how to predict l.jpg
e-Commerce WorkloadsHow to Predict

  • Take business projections of volumes or users (include fudge factor)

  • Estimate transaction volumes and CPU/transaction

  • Convert to normalized unit such as MIPS

Relational databases l.jpg
Relational Databases

  • Sub-second (OLTP), decision support / data mining

  • Distributed gateways

  • Database machines

  • Redundant data with extracts

  • How to predict: estimate a factor over current database demand or take usage estimates

Mainframe class unix l.jpg
Mainframe-Class Unix

  • Types: Mainframe USS or Linux, Future UNIX vendor offerings

  • Candidate applications

    • Web server

    • Vendor-ported applications

    • User-ported / new applications

  • How to predict:

    • Estimate by timeframe

    • Add factor to growth rates

Multiple platforms l.jpg
Multiple Platforms

  • Mainframe: plan like existing applications (#users, transactions * CPU/transaction, application look-alikes, sizing tools)

  • Distributed: use vendor sizing, modeling tools, existing applications

  • Network: use network simulation tools, rules-of-thumb, bandwidth calculations

New characteristics l.jpg
New Characteristics

  • External users

  • Continuous availability

  • New user interfaces

  • Cross-platform

External users l.jpg
External Users

  • Drive need for continuous availability

  • Different access patterns (e.g., doctor’s office vs. call center)

  • Service level measurement - harder to put agent on external workstations

Continuous availability l.jpg
Continuous Availability

  • Driven by external users

  • 24x7 schedule

    • Application redesign

    • Data Sharing: CPU overhead

    • Coupling Facility

    • Expansion of “prime shift”

  • 99.999% “up time”

    • Redundancy, overhead

    • Availability reporting

User interfaces l.jpg
User Interfaces

  • TCP/IP - no “definite response” (end-to-end response time measurement)

  • Multiple internal transactions per “mouse click”

  • Response time measurement:

    • Agent on workstations

    • Scripting from “robots”

Cross platform applications l.jpg
Cross Platform Applications

  • Only unified view: simulation package

  • Each platform (“silo”) can be analyzed separately.

  • Different application development groups

  • May be able to cross-validate user numbers

Types of implementation 1 l.jpg
Types of Implementation (1)

  • Standalone / “shrink-wrap”

  • Layered onto legacy applications

    • New mainframe application code

    • GUI front-end

    • Browser

    • Middle-tier (Unix or NT)

    • MQSeries - can add middle-tier and new mainframe applications

Types of implementation 2 l.jpg
Types of Implementation (2)

  • Legacy extracts

  • Re-engineered legacy applications

    • Convergence of business rules / applications

    • Re-usable components

    • Redundant access

    • Salvage investment, fix Band-Aids

    • Simplify logic, reduce platform complexity

What are we analyzing mainframe l.jpg
What Are We Analyzing?(Mainframe)

  • MIPS - growth, latent demand, software cost

  • Memory - track and watch 2 GB limit on central storage (goes away with 64-bit)

  • I/O - channels, gigabytes of disk, tape

  • Coupling Facility - Parallel Sysplex, Shared Data, continuous availability

  • Vendor upgrade paths

  • New partitions

What are we analyzing distributed l.jpg
What Are We Analyzing?(Distributed)

  • Number and types of platforms

  • CPU, memory, disk space

  • Bandwidth

  • Location of applications / processes

  • Platform limitations (CPU, memory)

  • Software pricing considerations

  • Porting opportunities

Measurement of new workloads l.jpg
Measurement of New Workloads

  • Summarize by platform:

    • Workload rules (process or user names)

    • Processes by descending CPU%

  • Resources: CPU, memory, disk space, Coupling Facility, network traffic

  • Growth:

    • Resources/user/application

    • Number of users + application changes

Distributed approach l.jpg
Distributed Approach

  • Consider tiers of service (not currently at Capital One)

  • Address service level measurement issue

  • Implement reporting

  • Add to Capacity Plan

  • “Silo” vs. “Application”

Tiers of service platinum l.jpg
Tiers of Service“Platinum”

  • Most expensive

  • Modeling product

  • Install in one server for each major application, use collection product for other servers

Tiers of service gold l.jpg
Tiers of Service“Gold”

  • Collection product

  • Capacity planning with Rules of Thumb

Tiers of service brass l.jpg
Tiers of Service“Brass”

  • Least expensive (man-hours only)

  • “Native”

    • Unix scripts

    • NT PerfMon

Service level measurement l.jpg
Service Level Measurement

  • API call at workstation - “Applications Response Measurement” (ARM) or Windows 2000 trace API calls

  • Agents: software tracing of Windows API calls - can be installed in a subset of end-user base (sampling)

  • Scripting (“robots”)

  • Stop watch sampling and logging

Scope of analysis l.jpg
Scope of Analysis

  • Silos

    • Look at each hardware/application environment independently.

  • Applications

    • Look at each application as a whole.

    • Application instrumentation

    • Inference: put platform silos together.

Analyzing the data growth rates l.jpg
Analyzing the DataGrowth Rates

  • General list of business plans

  • List of technical scenarios

  • Timeline

  • Estimate median and maximum likely MIPS/CPU/users/business units

  • Derive scenario growth rates

Analyzing the data additional resources l.jpg
Analyzing the DataAdditional Resources

  • Parallel Sysplex (Coupling Facility): important for continuous availability, level set functionality

  • Disk / channels / tape: disk megabytes, channel maximum, tape connectivity

  • Communications connectivity: new partitions for availability

  • Memory: 2 GB constraint, 64-bit

Growth l.jpg

  • “Baseline” growth

  • “Scenario” growth

  • Independent events (merger/acquisition, potential major project)

Example 1 mainframe upgrade l.jpg
Example 1: Mainframe Upgrade

  • Task force, led by Capacity Planner

  • Driven by expiring three-year lease (CPU replacement, three-year planning horizon)

  • “Vendor parade” - presentations and dialogues

    • Upgrade paths

    • Technology / service differences

    • References / site visits

    • Capacity sizing: MIPS charts, LSPR / sizing tools

Mainframe upgrade deliverables l.jpg
Mainframe Upgrade Deliverables

  • Document

    • Business drivers and technical scenarios

    • Growth forecasts

    • Vendor options and growth paths

    • Coupling Facility / Parallel Sysplex

  • Evaluation

    • Difference thresholds: MIPS claims, price/MIPS, ICF

    • Differentiators

Business and technical l.jpg
Business and Technical

Technical Scenarios

Consolidation of distributed servers

Continuous availability

Significant external business

Data Warehousing


Business Drivers

Cost management

External business

Improved data access

Business expansion

Projections l.jpg

  • Make educated guess by timeframe for each scenario

  • Add to “baseline” growth

  • Convert to growth rate

  • Use both “baseline” and “scenario growth”

  • Compare maximum scenario growth to maximum for platform family

Scenario timeline l.jpg


Initial muck exploitation with 250 Users

First Parallel Sysplex exploitation


First mainframe Wk1 Application


(Potential acquisition)

MajorProject A with 100 users, 150% CAGR

New DB2 functionality exploitation


64-bit OS/390

Full Data Sharing exploitation (IMS, CICS, DB2)


Full subsystem redundancy (IMS, CICS, DB2)


24x7 operation


Scenario Timeline

Vendor upgrade paths detail l.jpg
Vendor Upgrade PathsDetail

  • Use logarithms:

    Start*CAGR^x = Threshold

    x years = log(Threshold/Start)/log(CAGR)

  • Model MIPS MSU +40%/Yr +25%/Yr

    • GS2068E 952 160 Aug-00 Sep-00

    • GS2074E 1013 171 Oct-00 Dec-00

    • GS2084E 1141 193 Apr-01 Jul-01

    • GS2094E 1260 213 Sep-01 Dec-01

    • GS2104E 1378 234 Nov-01 May-02

Example 2 unix modeling l.jpg
Example 2: UNIX Modeling

  • Modeling product installed on MQSeries server

  • Application running with a known number of users

  • Projected rollout schedule used to drive model

  • Mainframe side: CICS application, IMS load

Unix platform workloads l.jpg
UNIX Platform Workloads

  • Two primary workloads:

    • MQSeries userids (mqm*) - memory intensive

    • Messaging application processes (MDA*) - “CPU intensive”

Workload modeling methodology l.jpg
Workload Modeling Methodology

  • MQSeries - Calculate relative workload intensity, enter model ratio.

  • Messaging application processes - Keep constant until application is removed from platform (“design loop” - always uses 1 CPU). Must adjust across CPU upgrade to continue using 1 CPU.

Track across upgrade l.jpg


Track Across Upgrade

Model presentation l.jpg
Model Presentation

Timeframe: April 2000

#Users: 180, 100

Ratios: 1.27, 1.00

Config: F50/02,2GB

Comment: Add Event1 Users

Validation tracking users on mainframe l.jpg
Validation - Tracking Users(on mainframe)





data ecld1;

format date date.;

format dt datetime.;






if recnum =: '99999' and rectype =: 'TCSCONFG';

dt = datetime();

date = datepart(dt);

hour = hour(dt);

data ecldpdb.users;

update ecldpdb.users ecld1;

by date hour;

proc print;

title 'Ecloud1 Users';

Example 3 server replacement l.jpg
Example 3: Server Replacement

  • Project: replace “old” NT servers

  • Application: Imaging servers

  • Capacity sizing data:

    • Rules-of-thumb analysis by vendor, using projected claims/minute and processor clock speeds

    • Benchmark information

Server replacement process l.jpg
Server Replacement Process

  • Multiple servers: each server is a workload, must be sized separately.

  • Enumerate and measure servers.

  • Apply growth rates and determine processing power requirements for the replacements.

  • Research available configurations and order appropriate server configurations.

  • Track CPU utilization across the upgrades.

  • Update relative capacity specs for next upgrade.

Server sizing l.jpg
Server Sizing

  • Find (or derive) benchmark capacity ratings for starting and replacement configurations.

  • Apply an estimate of current CPU utilization, a growth percentage, and a “peak/average” and performance buffer (+100% for this study).

  • Output: estimated percentages of a standard configuration. The number of estimated CPUs needed (23) came very close to the vendor’s original number of 24.

Example 4 hundreds of servers l.jpg
Example 4: Hundreds of Servers

  • Data capture

  • Reporting

  • Business drivers

Data capture l.jpg
Data Capture

  • Time-based scheduling product

  • Script-based data “pull”

  • Issue: data loss, time to find and rebuild

  • Potential fixes:

    • Product

    • Data “push” from servers

Data reporting analysis l.jpg
Data Reporting, Analysis

  • Color-based “health index” (Concord NetHealth metric).

  • Statistical Analysis (over two standard deviations from mean)

  • Thumbnail drilldown graphs

  • Automatic generation of html

  • “Treemap” graphs

Health index l.jpg
Health Index *

* Concord NetHealth metric

Automatic generation of html l.jpg
Automatic Generation of Html

  • Driven by “matrix”

    • Originally spreadsheet

    • Converted to relational database

    • Ultimate capacity planning solution: information by server, application, platform, business driver

  • SAS code - builds web pages and hyperlinks

Treemap l.jpg










Paper by Ben Shneiderman, University of Maryland,

Business drivers l.jpg
Business Drivers

  • Capacity Councils - business units responsible for capacity planning of “demand” side

  • Capacity Planners - build projections based on business drivers and historical trending

Business driver based forecasts l.jpg
Business Driver Based Forecasts











Regression analysis l.jpg
Regression Analysis

Input = CPU and Business Drivers by month

Output = Coefficients








By month (input = Widgets, Gadgets, Customers):

projection =Widgets*f1 + Gadgets*f2 + Customers*f3;

Graphical output l.jpg
Graphical Output

Widgets Gadgets Customers

Summary issues l.jpg

  • Access patterns and schedules

  • Platforms (more types and numbers)

  • Resources (what to track)

  • Levels of capacity management

  • Reporting of utilization and service levels, for large numbers of platforms

  • Higher availability (redundancy, reporting)

  • Deriving and reporting projections

Summary deriving projections l.jpg
SummaryDeriving Projections

  • Basic capacity planning:

    • Growth rates

    • Upgrade thresholds

  • Aggressive estimate of “scenario” demand

  • Bracket growth:

    • Lower end: “baseline”

    • Upper end: “scenarios”

Summary types of projections l.jpg
SummaryTypes of Projections

  • Number of transactions

  • Number of users

  • Number of platforms

  • Application sizing input

  • Application complexity

  • Fraction of an existing workload

  • Growth rate

Summary capacity planning l.jpg
SummaryCapacity Planning

  • Projections based on application and platform

  • Levels of capacity planning service

  • Report on all enterprise resources

  • Organize data with “matrix” database