Continuing the previous post subject I looked at another research about APM, which was made a bit earlier in 2010 by Forrester Research, Inc. and called
“Competitive Analysis: Application Performance Management And Business Transaction Monitoring”. The research can be downloaded here.
I found that research also admits importance of usage for APM the “self-learning” related techniques and treated that as a part of CEP - Complex Event Processing.
Based on the research,
“..The Next Step: APM, BTM, BPM, And CEP Converge Complex event processing (CEP) is most probably the first step in the evolution of application performance management. All products reviewed are using some form of statistical-based analysis to distinguish normal from abnormal behavior of applications and transactions. Nastel seems to have taken this analysis one step further by adding a level of inference to its solution. Progress Software has already made the jump into CEP by combining its expertise in BTM and BPM. OpTier recently acquired a solution and announced its intention to enter the advanced field of CEP. SL Corporation, based on its process control automation past, has provided event correlation for a long time, and further integrates with major CEP vendors…”
Below are Vendors that Forester’s research mentioned as having some CEP features (Underlined)
BPPM Application, Database, and Middleware Monitoring with Analytics monitors transactions running through Web application servers and messaging middleware as well as packaged applications like SAP, Oracle Applications, PeopleSoft, and Siebel CRM. Data collected is automatically integrated with a self-learning analytics engine.
AppManager Performance Profiler is a self-learning, continuously configuring, and continuously adapting technology that profiles dynamic application behavior and sends Trusted Alarms that helps troubleshoot system incidents.
..(Tivoli) proactively defines autothresholds based on normal behavior.
AutoPilot CEP integrates events from AutoPilot and third-party monitoring solutions to provide a predictive analysis of application and transaction behavior (normal versus abnormal) and provides a role-based dashboard.
RTView Historian allows for persistence of performance metrics via relational databases. The historical data is used for predictive analysis of trends in component and application behavior; historical data provides the ability to create trusted alerts triggered not against fixed thresholds but against dynamically calculated baselines that take into account typical loads during different periods of the workday.
SharePath builds a transaction model for each transaction type to show how it typically utilizes the infrastructure and then creates automatic baselines to provide alerting capabilities and information about a deviation from normal operating tolerances.
Progress Apama (also part of the RPM Suite) can take information from Actional and perform complex pattern detection activities around it, looking for anomalies that Actional might not otherwise detect. This might include, for example, detecting a cross-correlation between different transactions that might be the root cause of an issue.