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Mechanism for Operational Risk Monitoring, System Testing Optimization and Root Cause Analysis for Complex Applications

IP.com Disclosure Number: IPCOM000237767D
Publication Date: 2014-Jul-09

Publishing Venue

The IP.com Prior Art Database

Abstract

Software solutions often suffer from the following problems which is being addressed in this publication: A. Monitor operational risk and reduce impact of production anomalies B. Improve testing accuracy between testing and production phases C. Improve root cause analysis of problem systems

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Mechanism for Operational Risk Monitoring , System Testing Optimization and Root Cause Analysis for Complex Applications

Software solutions often suffer from the following problems which are being addressed in this invention:


A. Monitor operational risk and reduce impact of production anomalies


B. Improve testing accuracy between testing and production phases


C. Improve root cause analysis of problem systems

Enterprise systems are becoming extremely complex. Enterprise systems embrace more programming  models and include more middleware with increasing set of capabilities. Enterprise systems use more  layers of computing through virtualized and cloud platforms. Enterprise systems continue to add more  access channels such as smart phones and sensors. Enterprise systems are experiencing radical growth  of big data and associated analytics. Economic realities further drive complexity with more partner  enterprises involved in solution delivery.

Traditionally Enterprise systems are composed of many applications. In many cases, an application is not  fully tested under conditions similar to the conditions in production. The reasons for testing and  production differences usually include cost, skills, and time. The following are only examples of  differences that often exist between the testing (i.e. pre‐production) and production environments:


 Number and types of user requests


 Different qualities of service enforced in production


 Different protocols


 User request data


 System of record data


 Network capacity


 Mixing online requests with database batch jobs


 Performing administrative tasks in production such as taking database backups


 Production APIs or services are not fully available in testing.


 The application executes in a shared environment with hundreds of other applications


 Unforeseen outages.

If such differences are not taken into account, a production solution may experience service level  agreement problems such as a long response time or slow down. 

This problem is further exacerbated with enterprise systems becoming more complex as companies do  not fully understand how or what to test.  Applications are being delivered into production less tested  simply because the tester doesn't understand how to test everything.

Untested applications are deployed into production and wait for outages, then move into crit‐sit mode.  Commercial products are released with inadequate level of testing and rely on customer PMRs to debug 

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and fix problems. The feedback to testing on what to improve to make production system behavior more predictable is usually simplistic. 

Root cause analysis of outages or ...