Brian Fisher discussing using scAPM to Predict & Dynamically Report on KPIs

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IT: Customized to Your Advantage
Using scAPM to Predict &
Dynamically Report on KPIs
BRIAN FISHER
Senior Enterprise Monitoring Consultant
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Introduction
 Brian Fisher
 Atlanta, GA
 Senior Enterprise Monitoring Consultant
 Prolifics
 I am a Tivoli solutions consultant with more than 7 years experience in the
IT industry working with top-tier companies. I am experienced in many
aspects of the Tivoli monitoring portfolio including system, transaction,
and application management. I have extensive experience monitoring
application infrastructures including front-ends, middle-ware, and back-
ends within a SOA or BPM environment.
 I will be discussing my experiences in delivering the SmatCloud APM
solution at TBC
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Monitoring SOA-Based Applications
 End-to-End Transactions
 Linking Transactions within a Single Domain
 One message flow calling another message flow within IIB (aka WMB)
 Stitching Transactions across Different Domains
 A web service hosted on DP which calls a message flow within WMB which puts a
message on a MQ queue
 Aggregating Response Times & Failures from Different Perspectives
 Server (i.e. wsdp01, wsmb01)
 Component (i.e. DP, WMB, MQ)
 Applications (i.e. EG – CUST_VEHICLE_MGT_01, ORDER_MGT_01)
 Transactions (i.e. VehicleInspection, PurchaseOrderMgt)
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Monitoring SOA-Based Applications
 Web Services
 Service Operations
 Response Times
 Avg, Max, Min, Std Deviation
 Message Size
 Avg, Max, Min, Std Deviation
 Fault Counts
 Total
 Web Transactions
 Web Applications
 Response Times Breakdown
 Client, Network, Server, Resolve, & Render Times
 Errors
 Client & Server Side
 Retransmissions (aka Retries)
 Total
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Predicting Transactions Response Times
 Tivoli Performance Analyzer (TPA) & SPSS
 Nonlinear Trending
 Uses the Expert Modeler which automatically identifies and estimates the best-
fitting ARIMA or exponential smoothing model
 Exponential Smoothing Models
 Simple (no trend or seasonality)
 Holt’s Linear Trend (linear trend and no seasonality)
 Brown’s Linear Trend (special case of Holt’s model)
 Damped Trend (linear trend that is dying out and no seasonality)
 Simple Seasonal (no trend and a constant seasonal effect)
 Winter’s Additive (linear trend and a seasonal effect ind. on the level)
 Winter’s Multiplicative (linear trend and a seasonal effect dep. on level)
 Autoregressive Integrated Moving Average (ARIMA)
 aka Box-Jenkins Model
 Non-seasonal or seasonal
 With or without a fixed set of predictor variables
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Intelligent Reporting via Cognos Events
 Cognos Events
 Select Report Model
 ITCAM for Transactions (Query)
 Specify the conditions that will define this event
 Aggregates.Total_Time >= 10000
 Specify the report to run and any additional tasks
 Transactions (Total Time >= 10s)
 Output as HTML & PDF
 Send an email with report link and PDF attached
 Schedule check intervals
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Summary
 Using scAPM you can …
 Track transactions end-to-end
 Gather key performance indicators
 Forecast these KPIs using NLT
 Dynamically trigger reports based upon historical criteria
 Using scAPM TBC was able to …
 Reduce MTTR via quicker problem identification
 Increase service availability by correcting problems with the ERP backend
that were isolated
 Increase service responsiveness by correcting problems with error
handling that were detected
 Increase customer satisfaction by reducing the time the application is
perceived as “spinning”
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