Understanding the Performance Management Process

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1 Understanding the Performance Management Process

2 Monitoring Market Monitoring tools account for more then 50% of market Most organizations have not matured their monitoring environment Missing Process Complete view Linkage of tools/data Monitoring in Demand 2004 Management tool study Priority Category What priority for monitoring tools of 23 management categories Companies confuse volume of tools and successful use 2

3 Succeeding With Monitoring Must overcome fragmented purchasing behavior Centralization of data Link disparate technical silos via shared process Shared problem identification and resolution central to customer satisfaction Singl Tech Silo Centralization Occurring 2004 Management Tool Deployment Plans Single BU/Proj. Consolidated/Ops Outsourcers No clear pattern Users are demanding monitoring data in context, leading to a desire to centralize information 3

4 The Criticality of Processes Process leads to efficiency Efficiency leads to reduced cost Process usage increasing across IT organizations Investment has focused on change and configuration Each process requires an owner Without Process Most Organizations Fail Reacting to problems Purchasing and deployment management technology Optimizing organizational structure Implementation of ITIL based processes Implementation of standard processes All Primary Successful organizations have implemented processes implemented via tools 4

5 Monitoring Process Goal maximum performance of technology and quickest response to issues A distinction between health/availability monitoring and performance monitoring h/a = is it alive and limited metrics Perf. = analysis of data extensive data for improvement and resolution Process must be Reactive alarms/alerts/events Proactive - analysis for planning and improvement Must be larger then single technology element 5

6 Monitoring: A Practical View Reporting Fault Monitoring Performance Monitoring Collect Data Action Change Capacity Planning Define Policy Historical Data Policies Analysis Generate Event Incident Correlate Events Config Config Database Performance Improvement BRM SLM Correlation Models Commonly missing linage to incident, resulting in fragmented actions being taken and no tracking 6

7 Normalized Monitoring Monitoring Action Reporting Collect Data Change Capacity Planning Historical Data Analysis Config Define Incident Policy Policies Config Database Performance Improvement BRM SLM Correlation Models Move event generation and event correlation into analysis simplifying activities and normalizing fault and performance activities 7

8 Monitoring Feeds for Critical Processes Processed data stored for future analysis Monitoring Raw data for analysis Historical data for analysis Rules for data analysis (base on business policies) Collect Data Historical Data Policies Analysis Change Generate an automated request for change Processed data generated an incident for escalation Incident Performance and availability data to feed SLM SLM Correlation Models Analysis is the key to all monitoring Rules for data analysis (based on configuration data) 8

9 Peering Inside the Analysis Task Collect Data Receive Raw Data Historical Data Expected Behavior Apply Algorithms Anomaly? NO Request Change? YES Change YES Policies Analysis Rule Generate Event Correlate Events Incident SLM Correlation Models Algorithms will determine the depth and type (e.g., failure, statistical, time-series) of analysis 9

10 Handoffs Process boundaries will vary by company or even within a single business unit or sub IT group Handoffs to other processes requires passing of context The most difficult part of linking processes is sharing data Often tools will span multiple processes Linking processes leads to increased efficiency and reduced costs 10

11 Enter Capacity Management The planning of capacity needs and validation of resource utilization as related to the plan Commonly confused with trending (trend lines) Modeling is crucial to true planning Capacity management a cornerstone process in new adaptive efforts Highly redundant to have separate capacity management and performance management Data should be shared Capacity management is increasing scope beyond single devices 11

12 Process: Capacity Management Baseline Environment Workload Model Characterize Workload Measure and Modify Workload Model Identify Cost Components Service Level and Resource Performance Measures Performance Model Produce Workload Forecast Define Performance and Availability Capabilities Service Level Performance Predictions Define Cost Model Supply Cost Estimates Cost Analysis Configuration Management Budget Management BRM Resource Plan 12

13 Enter Monitoring Tools A monitoring process is only as good as the data that is feeding it Must exert effort to setting or validating thresholds and actions Will have multiple monitoring tools Acceptable as long as integration is understood Most common to have silo d tools for specialty hardware Centralize around common infrastructure or applications (e.g., all unix, all J2EE) New efforts have begun to create performance repository/warehouse for future analysis 13

14 The Three Tier Monitoring Model Tier 1 Data Collection Data Collection Numerous tools Data gathering for specific elements or for response time Domain specific tools Some consolidation Goal to minimize vendors for like technology Tier 2 Mid Level Manager Mid Level Manager One per domain Centralization of alerts and data for a specific silo e.g., NOC, application Correlation and analysis occur for the specific domain Tier 3 Manager of of Managers Few per enterprise A fourth layer emerging for reporting Enterprise wide centralization of key alert data Enterprise wide correlation, notification, and escalation Can be an event view, business view or service level reporting 14

15 Assessing Monitoring Maturity Self assessment Identify gaps in data collection Have vendors been minimized? Is data from elements and response time being integrated Real time? Historical? Is it leveraged for other functions? Is end user perspective accounted for? Is data being correlated? At how many levels? How are results being leveraged? Does an enterprise view exist? Is there integration across other processes? 15

16 Bottom Line Success with monitoring investments will increase with a investment in monitoring processes Ensure processes are linked to other appropriate operational activities Share data across functions Understand the tool deployment logic and avoid overlap and unnecessary investment 16

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