Scott Chapman – American Electric Power Paper 9015 Session 331.

Scott Chapman – American Electric Power

Paper 9015Session 331

Agenda

What I mean by “critical path” My simple way of finding it Review some sample code Questions Bonus Material

Geek-required xkcd reference

HTTP://XKCD.COM/399/

Critical path

Simple definition: how long is this going to take? Longest sequence of activities

In a project In a batch schedule

Need to look at: Predecessor-successor relationships Durations Time dependencies

Ideas originated in Project Management

Project Management History Critical Path Method (CPM) originated at

DuPont in 1950s Used to manage chemical plant

maintenance projects Critical path is the sequence of events which

determines the duration of the project Delays in tasks on the CP delay the entire

project CP must be managed to stay on schedule To finish project earlier, tasks on CP must be

somehow shortened

CPM process

Identify activities Identify sequence and dependencies Draw network diagram of activities Estimate duration of activities Identify the longest path in the network

(critical path) Monitor & update as project progresses

Delays in tasks not on CP may change the CP!

Batch Windows & CP Business runs on cycles

Daily, weekly, monthly processes Large applications have large batch

schedules Batch schedules can be drawn as a

network diagram Predecessor – Successor

relationships between jobs Like projects, we like our batch

windows to finish on time! Like a project, a batch schedule has a

CP

CP Calculation Formally:

CP is path with no slack for any task Slack = difference between earliest & latest

start or finish time of task Latest finish = latest time task can finish

without delaying project And how do you figure that???

Informally: Find all the paths through the network

diagram Add up task duration on each path Select the longest path

Simple example

Five paths through this simple example

Task A

Task D

Task B

Task C

Task E

Task F

Start

End

Simple example

Calculate path durations from task durations

Task A

Task D

Task B

Task C

Task E

Task F

Start

End15 mins

30 mins

20 mins

15 mins

10 mins

5 mins

A+B+E = 45A+C+E = 40A+C+F = 35D+C+E = 55D+C+F = 50

So that’s simple enough!

Everybody ready to leave?

Real world complications Hundreds or thousands of batch jobs

Managed by a batch scheduler package

Time-of-day dependencies Extraneous dependencies

New jobs added without cleaning up obsolete dependencies

Variable execution times Variation in data to be processed Contention with other processes External waits Job failures

What that might look like…

Winding your way through that mess is a bit more complicated!

Tooling Options• Package from scheduler vendor

+ Should be well integrated

- Cost?

• Microsoft Project- Not really meant for this purpose

+ See CMG Proceedings: Schwarz/Aurand, 1999 and Zaslavsky, 2001

• SAS/OR- Cost and effort?

• Roll your own+ Can make output exactly what you want

- Time / effort

+ Sample code on your CD!

What is the real question?1. What is the longest path through

the schedule?- prediction of the critical path- usually one-time analysis

2. Why did job X finish late last night?- an ongoing question / process- requires the CP for job X

Fortunately, #2 is much easier!

Use what you know

Predecessors (from batch scheduler)

End times (from actual executions)

We are answering a question, not predicting the future

We just need to explain what happenedLook at the jobs in the critical path for job

X for anomalies

Critical path simplified Start at job X Find the predecessor that ended last

– that was the critical predecessor to X Call that job W

Find last predecessor of W, call it V Repeat until:

You go back some number of levels or You reach a time dependency

Resulting list is the critical path, for the day under study, for job X

Simple example

Task A

Task D

Task B

Task C

Task E

Task F

Start19:00

End15 mins

30 mins

20 mins

15 mins

10 mins

5 mins

19:15

19:30

19:35

19:45

19:55

19:50

Working backwards…

Task A

Task D

Task B

Task C

Task E

Task F

Start19:00

End19:15

19:30

19:35

19:45

19:55

19:50

E ended last

What is E’s last predecessor?

Task A

Task D

Task B

Task C

Task E

Task F

Start19:00

End19:15

19:30

19:35

19:45

19:55

19:50

E ended lastC ended after B

What is C’s last predecessor?

Task A

Task D

Task B

Task C

Task E

Task F

Start19:00

End19:15

19:30

19:35

19:45

19:55

19:50

E ended lastC ended after BD ended after A

Critical Path is E – C – D

Task A

Task D

Task B

Task C

Task E

Task F

Start19:00

End19:15

19:30

19:35

19:45

19:55

19:50

Tasks A, B, F had no direct bearing on the end time

Complicating simplicity… Schedule changes every day

Weekly / monthly processing Application changes

Schedule relationships may not be pristine

Jobs may be run multiple times—be sure to use the correct instance

If you want to graph the entire schedule it gets more complicated

Why bother finding the CP? Limit the data you need to look at to

investigate a late-finishing job The cause is on the CP

Find changes If the CP changes day to day:

why? Investigate impact of periodic

schedule differences Addition of monthly processing

jobs may change the CP

What I do Capture job stats every day to a

performance database Standard practice Extract history daily to XML file

Extract schedule once per day and store for 45 days Saved as XML files Allows historical investigation

JavaScript browser application pulls both data sources and allows for investigation

Sample application Input #1: Schedule XML file<?xml version='1.0'?><?xml-stylesheet type='text/xsl'?><opc><app id='#AMCSMISCBILL'> <op id='87' job='#AMCS331' arr='1930'><wkstn>CPUJ</wkstn> <desc>Online Bill Image xtract</desc> <pred><aid>#AMCSMISCBILL</aid><opid>81</opid></pred>… <pred><aid>#AMCSMISCBILL</aid><opid>78</opid></pred> <succ><aid>#SMCSDAILYRPTS</aid><opid>3</opid></succ>… <succ><aid>#AMCSDLYBKUP</aid><opid>6</opid></succ> </op> <op id='90' job='#AMX1358' arr='1930'><wkstn>CPUJ</wkstn> <desc>Load O/L Bill Image</desc> <pred><aid>#AMCSMISCBILL</aid><opid>87</opid></pred> <succ><aid>#AMCSMISCBILL</aid><opid>91</opid></succ> </op></app>

A grouping of jobs is an

“application”

A job is an “operation”

Each job has predecessors

and successors

Arrival time is the earliest the

job can run

Sample application Input #2: Job Data XML file

<job id="#AMCS331"> <sys>COCJ</sys><cls>0</cls><desc>MCSX4000</desc><cnt>24</cnt> <acpu>2.18</acpu><aet>13.8</aet><mcpu>2.70</mcpu><met>26.7</met> <run i="2009-05-09 2:01:55" rse=" 1:59:46, 2:01:56, 2:02:09"> <et>13.7</et><cp>2.16</cp><io>21632</io> </run> <run i="2009-05-08 0:51:46" rse=" 0:51:45, 0:51:48, 0:52:02"> <et>16.5</et><cp>2.11</cp><io>21690</io> </run> <run i="2009-05-07 1:24:41" rse=" 1:22:04, 1:24:41, 1:24:52"> <et>11.4</et><cp>2.12</cp><io>21740</io><sys>CO1J</sys> </run></job>

Jobs by name here

Norms, averages, maximums

May have multiple runs per

day

Read, start, end times

Note relatively compact format designed to reduce the file size—unfortunately that increases the complexity of interpreting the data

Stats for single run

Sample application output

Compare days in two windows

Why later on 12/29?

Completely different CP here

Critical path comes back together here

Here’s an ET difference of >1 hour and CPU >2x increase!

Sample application details On CD at back of room Very simple example coded quickly one

Sunday afternoon May not be bug free Will not satisfy all your needs For illustrative purposes only – easier to

understand than the examples in the paper HTML / JavaScript application Data is in XML files Internet Explorer only Uses XPath & XSLT

Beyond the scope of this presentation

Application flow When HTML page loads

Calls init() to load the XML files Selection criteria populated in HTML

When user clicks “Find It” button Calls findIt() to find the critical path,

which in turn calls: getRunsDate(job, date) – returns array of

executions of a job on a given batch date getLatestPred(job, app) – returns

predecessor that ran last getRunsDate is an example of using DOM functions to extract data from XML

getRunsDate is an example of using DOM functions to extract data from XML

Critical parts of the code

findIt() Get runs for this job and date

Save stats for this job to array

Then find preds and add them to

the array

Loop through array and build

HTML table

getLatestPred(cjob, capp)XPath to get

array of preds

Use DOM calls to get data from

XML

Xpath to find job name from op id

Get the runs for a pred job

Check run to see if it is the latest

pred so far

If it is, save it

That’s essentially it!

getRunsDate(job, date) is nothing special – simply retrieves list of runs from the XML file

Typical housekeeping code initializing variables, etc.

Sample in the paper was much more complicated due to it being pulled from the application that does the graphing

Summary

Critical Path Analysis for Performance Analysis usually involves answering why, not predicting the future

In such a case, start at the end and work backwards

That type of analysis is easy to code Record a snapshot of the schedule

daily as well as the job performance

Questions / comments ?

Bonus material: Export to Excel! Sometimes you need to play with

your data Copy to Excel to

Re-sort Filter Pivot tables Graph Summarize

Cut and paste HTML tables works well

Even better: automate it

New & improved:

sendExcel() function on CD

If Excel not open, open it If previous workbook not open

Open a new workbook Add new sheet to work book with

data

Requires IE and Excel

Useful references

XPath, XSLT, XML quick reference cards http://www.mulberrytech.com/quickref/index.html

Browser Book for Web Designers http://www.visibone.com/products/browserbook.html

Humor for geeks http://xkcd.com/

Scott Chapman – American Electric Power Paper 9015 Session 331.

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Transcript of Scott Chapman – American Electric Power Paper 9015 Session 331.