|By Cloud Best Practices Network, PR Newswire||
|February 20, 2012 08:08 AM EST||
Troubleshooting ALM – Application Lifecycle Management
The value of their analytics tools can be quantified by first understanding the problem statement they address, most notably by focusing on the processes of ‘ALM’ – Application Lifecycle Management, where downtime has a lot to do with human error and configuration mistakes.
Development, QA and operations teams spend huge amounts of time involved in the manual aspects of IT, processes like Release Management, with each group spending between 30-75% of their time with associated work like building, configuring and maintaining test environments, on technical support for process moving applications through Development, Test and Production.
Furthermore 50% of all application issues are due to differences between test and production environments, 60% of data center application failures are the result of faulty configuration changes, with application problems typically increasing by 200% in the first 10 days following a release.
The IT Process Institute’s Visible Ops Handbook reports that “80% of unplanned outages are due to ill-planned changes made by administrators (“operations staff”) or developers.” (Visible Ops).
A recent Gartner study projected that “Through 2015, 80% of outages impacting mission-critical services will be caused by people and process issues, and more than 50% of those outages will be caused by change/configuration/release integration and hand-off issues.” (Ronni J. Colville and George Spafford Configuration Management for Virtual and Cloud Infrastructures).
The impact is clear – Manual configuration errors can cost companies up to $72,000 per hour in Web application downtime.
Cloud Configuration Management – Quality control for DevOps
One of the key goals and perceived benefits of Cloud computing is a simplified IT environment, a reduction of complexity through virtualizing applications into a single overall environment, and by providing management tools so that these kinds of issues can be eliminated.
However complexity actually increases. Virtual Machines (VMs) encapsulate application and infrastructure configurations, they package up a combination of applications and their settings, obscuring this data from traditional configuration management tools.
Furthermore the ease of self-service creation of VMs results in their widespread proliferation, and so actually the adoption of Cloud technologies creates a need for a new, extra dimension of systems management.
This is called ‘Cloud Configuration Management’, modernizing this skill set for virtualized Cloud environments, where the Evolven software can covers the entire IT environment – the wide variety of applications and their underlying infrastructure, including: applications, front-end servers, middleware, databases, messaging layer, operating systems, virtualization layer, and hardware.
It can dive to granular level, drilling down to the most granular level of the individual configuration parameter in any configuration source, including configuration files, registry, database schema, stored procedures, and reference data held in the database. From this it delivers actionable information – applying powerful analytics to classify configuration changes by impact and criticality enabling :
- Release & Incident Management - The increased complexity therefore increases the difficulties in trouble-shooting technical problems, and thus requires an updated set of tools and also updates to best practices like the use of ITIL procedures.
- Defect Analysis - Not only can this reduce MTTR, but also it can provide the QMS feedback loop required for DevOps.
‘Release into Production’ is a particularly sensitive process within software teams, as major upgrades and patches are transitioned from test to live environments. Any number of configuration-related errors could cause the move to fail, and so CCM software delivers the core competency of being better able to respond quicker to identify and resolve these issues, reducing the MTTR significantly.
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