pulse 2014 apm user group - scapm to predict & dynamically report on kpis
DESCRIPTION
Brian Fisher discussing using scAPM to Predict & Dynamically Report on KPIsTRANSCRIPT
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Using scAPM to Predict & Dynamically Report on KPIs
BRIAN FISHERSenior Enterprise Monitoring
Consultant
Public | Copyright © 2014 Prolifics
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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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