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/

Prediction for Business Service Assurance

Artificial Intelligence for IT Operations (AIOps) uses technologies like Big Data and Machine Learning (ML) to automate the resolution and identification of the common problems of Information Technology (IT).

Zero Incident Framework (ZIF) is an AIOps based TechOps platform which enables proactive detection and remediation of incidents which helps an enterprise to move towards a Zero Incident Enterprise.

Five modules of ZIF:

• -        Discover: To auto-discover all the IT assets and mission-critical workloads
• -        Monitor: End-to-end enterprise performance monitoring
• -        Analyze: Analyze and correlate alerts/events across tools
• -        Predict: Predictive techniques to prevent outages
• -        Remediate: Prescriptive remediation with minimal or no manual intervention

Predictive Analysis is ZIF’s USP. ZIF encompasses Supervised, Unsupervised and Reinforcement Learning algorithms for realization of various businesses use. They are:

                  Supervised Learning Algorithm:

·        Linear prediction

·        Seasonality based prediction

·        Recommended resolution

·        Virtual Supervisor

·        False Probability

·        Anomaly Deduction

·        Estimated Time to Complete

Unsupervised Learning Algorithm:

·        Sequence-based mining

·        Keyboard extraction

·        Sentiment Analysis (CSAT)

·        Data labeling

·        Log Analytics

-        

                        Reinforcement Learning Algorithms:

·        Log Rotation

·        Optimizing resource utilization

How does ZIF work:

-        

•       ZIF can receive and process all kinds of data through its ingestion capabilities
• -        Predicts anomalies by analyzing these data
• -        Anomalies can be presented as ‘Opportunity cards’ to eliminate any undesired incidents
• -        It brings out the paradigm shift since its proactive and reactive
• -        Predictions occur at multiple levels

Sub-Functions of the Predictive Model:

Functions:

-        Forecast capacity needs

·        Provides accurate resource utilization and usage predictions

·        Orchestrates the virtual infrastructure

-        Forecast Incident Volume:

·        Predicts to spike and exceed capacity

·        Predicts corrective actions

-        Detects potential failures:

·        Forecasts when capacity is about to exceed

·        Identifies problems even when they originate somewhere else

Benefits:

• -        Reduced manual effort
• -        Reduced errors
• -        Reduced cost of operations
• -        Optimized staffing
• -        Proactive IT Operations
• -        Drive towards zero outage
• -        Enhanced user experience

Predict module can categorize the opportunity cards into three swim lanes. They are:

• -        Warning swim lane: Opportunity Cards that have an “Expected Time of Impact” (ETI) beyond 60 minutes.
• -        Critical swim lane: Opportunity Cards that have an ETI within 60 minutes.
• -        Processed / Lost: Opportunity Cards that have been processed or lost without taking any action.

The above article has been taken from Zero Incident Framework, that is the best tool for predictive analytics using ai applications, and a leading cognitive process automation tools for business

Zero Incident Framework Corporate Brochure

Zero Incident Framework (ZIF) is a TechOps platform that is AI-based. ZIF helps in the remediation and proactive detection of incidents which in return helps an Organization to become a Zero Incident Enterprise.

Basic components of Zero Incident Framework (ZIF):

-        Discover

-        Monitor

-        Analyze

-        Predict

-        Remediate

Discover: Discovers all the critical mission workloads and IT assets.

• -        Auto-Discover Application
• -        Auto-Discover Users
• -        Real-Time Dynamic Topology

Monitor: Does the end-to-end performance monitoring for the enterprise.

• -        Allows visibility of the full stack
• -        Detects Anomalies
• -        User Experience Index, Application Health Index

Analyze: Analyzes and correlates alerts and events across tools.

• -        Ingest & Correlate heterogeneous Datasets
• -        Noise Nullification
• -        Accelerated RCA

Predict: Brings predictability to detect the potential outages.

• -        Forecasts the Capacity of Needs
• -        Forecast the Incident Volume
• -        Detects the Potential Failures

Remediate Prescriptive remediation with least or no manual intervention.

• -        Orchestration
• -        Auto-Remediation
• -        Virtual Engineer

ZIF for Service Desk:

• -        Enhances the productivity of the Service Desk by reducing the meantime to repair
• -        Takes user experience to the next level through the application of enhanced Sentiment Analysis and reduction of repeated incidents.
• -        Reduces the number of incidents as well as the cost of operations.

ZIF for Data Center:

• -        Reduces the false positives as well as provides a unified view of the health of the enterprise by ingesting and aggregating diverse data.
• -        Uses an Advanced Intelligent Incident Analytics to analyze the root cause faster.
• -        The predictive insights prevent the outage of expensive systems.

Outcome:

• -        Single Pane of Command
• -        Proactive IT Operations
• -        40% reduction of incidents
• -        60% reduction in Mean Time to Repair
• -        50% reduction in overall IT Operations costs
• -        Agile monitoring & elimination of Digital Dirt

Zero Incident Framework is the best tool for predictive analytics using AI applications, and for workflow automation software architecture. It ensures Zero IT downtime for your organization, and reduces manual efforts for the IT Personnel.

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.

Assess Your Organization’s Maturity in Adopting AIOps

AIOps stands for using Artificial Intelligence in IT Operations. AIOps is adopted by organizations to deliver tangible business outcomes. Artificial Intelligence for IT operations implies the implementation of true Autonomous Artificial Intelligence in ITOps, which needs to be adopted as an organization-wide strategy.

Thus, the implementation of AIOps requires evaluation and assessment of your organization’s current maturity state, existing landscape, processes, and more. This blog hereby focuses on 4 levels of maturity in AIOps adoption. Assessing an organization against each of these levels helps in achieving the goal of TRUE Artificial Intelligence in IT Operations. Below enlisted is a gist of these levels –

·        

• Level 1 – Knee Jerk - Events, logs are generated in silos and collected from various applications and devices in the infrastructure. The engineering teams work in silos, not aware of the business impact that these alerts could potentially create. Here, operations are very reactive which could cost the organization millions of dollars.
• Level 2 – Unified – Organisations have integrated all events, logs, and alerts into one central locale. SOPs have been adjusted since the process is unified, but this is still reactive incident management.
• Level 3 – Intelligent - Machine Learning algorithms (either supervised or unsupervised) have been implemented on the unified data to derive insights. If Mean Time To Resolve (MTTR) an incident has been reduced by automated identification of the root cause, then the organization has attained level 3 maturity in AIOps.
• Level 4 – Predictive & Autonomous – The highest level of maturity of AIOps is this level. If incidents and performance degradation of applications can be predicted by leveraging Artificial Intelligence, it implies improved application availability. Level 4 is a shift in IT operations – moving operations entirely from being reactive, to becoming proactive.

Zero Incident Framework (ZIF) provides for the best AIOps product tools and products. It is developed by GAVS Technologies. It uses AI to maintain the IT Operations of a business along with ensuring zero downtime. Read this blog to know more.