Addressing Web Application Performance Issues - Zero Incident Framework

It is important to make sure that the end-user gets a greater experience while using an application and therefore it is compulsory to monitor the performance of an application to provide higher satisfaction to them.

External factors

When the web applications face performance issues, here are some questions that need to be asked:

• ·       Application performance issue
• ·       Production environment
• ·       Release of the application stack
• ·       Hardware/software upgrades

 Actions

• Look at the number of incoming requests
• Identify how many requests are delaying
• Look at the web pages/methods/functions in the source code
• Identify whether any third-party links or APIs is making it slow
• Check whether the database queries are taking more time
• Identify whether the problem is related to a certain browser
• Check if the server-side or client-side is facing any uncaught exceptions
• Check the performance of the CPU, Memory, and Disk of the server
• Check the sibling processes which are consuming more Memory/CPU/Disk in all servers

 

Challenges 

People need to be well equipped with technologies across all layers to know what parameters to collect and how to collect.

Zero Incident Framework Application Performance Monitoring gives details of application performance management. The APM Engine has built-in AI features monitor the application across all layers, starting from an end-user, web application, to the underlying infrastructure.

The in-built AI engine does the following automatically: 

1.    Monitors the performance of the application (Web) layer, Service Layer, API, and Middle tier and Maps the insights

2.    Traces the end-to-end user transaction journey

3.    Monitors the performance of the 3rd party calls

4.    Monitors the End User Experience

Why choose ZIF APM?

Key Features and Benefits

1.    Provides a 360 insight into the underlying Web Server, API server, DB server related infrastructure metrics

2.    Captures performance issues and anomalies that the end-users face

3.    Offers deeper insights on the exceptions faced by the application

4.    Gives every detail about which method and function calls take more time or slow down the application

5.    Provides the details about 3rd party APIs or Database calls

6.    Analyzes unusual spikes through pattern matching, thus alerting providers

Read the complete blog by the ai for application monitoring tool, Zero Incident Framework - https://zif.ai/addressing-web-application-performance-issues/

Machine Learning: Building Clustering Algorithms

Clustering is a widely-used Machine Learning (ML) technique. Clustering is an Unsupervised ML algorithm that is built to learn patterns from input data without any training, besides being able of processing data with high dimensions. This makes clustering the method of choice to solve a wide range and variety of ML problems. Machine Learning and Clustering has been best explained by the best digital service desk AI software – Zero Incident Framework (ZIF).

ZIF is an award-winning tool developed by GAVS Technologies for the management of AIOps, AI automated root cause analysis solution, AI data analytics monitoring tools and many more such applications. Some excerpts from the blog are provided herein -

What is Clustering and how does it work?

Clustering is finding groups of objects (data) such that objects in the same group will be similar (related) to one another and different from (unrelated to) objects in other groups.

Clustering works on the concept of Similarity/Dissimilarity between data points. The higher similarity between data points, the more likely these data points will belong to the same cluster and higher the dissimilarity between data points, the more likely these data points will be kept out of the same cluster.

This blog also encompasses how a clustering algorithm can be built, how a dissimilarity matrix is built, properties of a distance matrix, and how it is built.

Considerations for the selection of clustering algorithms:

Before the selection of a clustering algorithm, the following considerations need to be evaluated to identify the right clustering algorithms for the given problem. Some of them are -

1.      Partition criteria: Single Level vs hierarchical portioning

2.      Separation of clusters: Exclusive (one data point belongs to only one class) vs non-exclusive (one data point can belong to more than one class)

3.      Similarity measures: Distance-based vs Connectivity-based

Clustering is broadly used in two applications namely - As an ML tool to get insight into data, and as a pre-processing or intermediate step for other classes of algorithms. Read this blog here to know more.