Pulse 2014 APM User Group - scAPM to Predict & Dynamically Report on KPIs

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Using scAPM to Predict & Dynamically Report on KPIs

BRIAN FISHERSenior 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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Date post:	18-Nov-2014
Category:	Technology
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Pulse 2014 APM User Group - scAPM to Predict & Dynamically Report on KPIs

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