DATA, NETWORK & APPLICATION MANAGEMENT

With constantly evolving IT, software applications are now complex, dynamic, and widely distributed than ever before. Application architecture too has also changed formats from standalone to client-server, then to mobile devices and cloud services. Complex application environments are not a concern of the end user. Customers expect the apps they use to work at all times, on all platforms, and anywhere in the world. Monitoring the performance of business-critical applications are hence terribly crucial to running a thriving and competitive business. An effective Application Performance Management system strives to stay current as the industry evolves. Application Performance Management works on the following four principles.

• Monitoring Compliance: The first step towards effective application performance management is effective monitoring. The first step to effective monitoring is knowing what to monitor, how to, and what to look for. Establishing effective monitoring compliance is critical to maintaining peak performance for your applications
• Prediction: Being able to predict when an IT Asset will fail is the first step toward improved application performance management. The first step towards prediction is aggregating monitoring, failure, incident, event, and alert data to build a predictive model that allows you to identify the conditions when an IT asset might fail. 
• Proactive Resolution: APM tools allow you to monitor applications, while prediction allows you to draw insights into how and when assets fail. But proactive resolution helps prevent the incident and essentially eliminates the downtime associated with asset failure.  
• Dynamic Thresholding: The biggest factor in effective application performance management is resource utilization – the human resources from the managed services teams that help keep your applications up and running. In order to make sure these resources are utilised efficiently, you need your monitoring and alerts mechanism to filter out the noise and false positives. This allows the resolvers to focus on the real issues. To achieve this, you need event correlation and real-time AI and ML-based analytics into alerts to reduce the noise, and also use proactive threshold modification to address known spikes in CPU, Memory, Network, and Storage utilization that typically throws alerts – this, in cases where these spikes are temporary or expected. 

To provide a seamless end-user experience, Application Performance Management includes:

• Monitoring your IT assets for CPU usage, memory, and read/write speeds.
• Tracking how often web requests end in error during memory-intensive processes and degradation of app performance.
• Measuring response time to help identify the effect of speed on app performance
• Dynamic resource allocation to cater to usage and request spikes
• Analytics into request rates viz, the traffic received by the app, spikes, inactivity, and number of active users to help in the optimization of your apps
• Monitoring application availability helps keep up with latency requirements
• Monitoring performance degradation of apps written in programming languages like Java that use garbage collection, which is likely to encounter issues due to its heavy use of memory.
• Tracking user experience, which increases the productivity of your IT environment by reducing time spent manually searching through event logs or building synthetic monitors.