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Revolutionizing NOC Operations
for the Telecom Industry

  • Company
Private Company
  • Domain
Telecom & Networking
  • Service
Solution Engineering
  • Technology
Angular, MongoDB, NodeJS, Financial 
   Tracker, Mobile, Android, Cloud
  • Platform
Mobile

Introduction 

The realm of digital has become essential to our daily lives. As the IT industry advances at a breathtaking pace, the need to revolutionise networking as it were is at its peak. We transcended beyond clunky, traditional networks to an age of customer focused solutions, and Network Operation Centres (NOC) have been at the centre of this transformation.
To put it simply, the NOC is the central nervous system of the mobile network infrastructure. It is not only the first line of defense against potential threats, but also ensures seamless data inflow and outflow. However, the idea that this is all driven by machines is a misconception, for if the NOC is the brain of the entire operation, then NOC operators are its heart. 

“The Pace Of Evolution In IT, Their Experience Of Working In Environments With An Absence Of Fully Functioning Network Capabilities”

Problem Statement

Simplifying NOC Operations and network states through automated, deep insight driven AI-ML systems 

Network Operation Centres are hubs of bustling activity when it comes to monitoring transactions, traffic, and user patterns. The NOC operator acts as a liaison between the customer and the network infrastructure to solve any issues related to the above. They are an invaluable resource in the technology puzzle, but are also amongst the most overburdened, owing to inefficient process management, lack of agile working environments and pre existing redundancies.
The traditional NOC system involves multiple dashboard screens parallelly in use, with each individual request catered separately. This involves a massive investment of time and effort for the operator, often coupled with slow delivery and issue resolution.

Overview

Multitasking like a Pro:
NOC engineers may find themselves juggling hundreds of requests at the same time. These ticketing requests are often unrelated to each other, and would need immediate resolution. Faults may range from server issues, software bugs or physical defects at the site of the network tower. Thus, the requirement for an automated, agile and intuitive NOC system is increasingly apparent in a digital first era.
The key requirements of a modernized NOC setup include :  

Auto suggestions for ticket key metrics

For reduced human error and analysis of assigned values

Automated alarm correlation and RCA

For faster alarm detection

Digitally managed KPIs and inventory insights

For a holistic view of network corresponding alarms

NOC operators working in a centralized location struggle to keep track of time, incident management and service resolution 24×7, and this is where the need for an end-to-end dissolution model comes in. Here is where the Design thinking approach can make a world of difference. A human centric approach to solving these complex problems not only enables deeper customer engagement but also allows for creative, critical solutions that go beyond just usability.
This is why AI & ML techniques come in handy – to reduce not just the number of tickets created per problem, but also dispatching them on time. Rule mining, problem classification techniques and time series predictions have been able to successfully decrease the time to dispatch for requests.
Keeping this in mind, the iauro team has developed a state of the art application to digitize network operations effectively. With microservices at its core, this application will be used by NOC operators and decision makers to primarily deal with identity management and email servers effectively. In this particular case, the approaches for each problem are tentative and would be finalized post the data analysis for alarms raised, incident management and KPI related requirements. 

“Common Challenges And Network Problems From An NOC Operator’s Perspective, The Role Of AI And ML In Enabling Agile, Multitask-Able Environments”

AI-ML Approaches top level view

App and Data Architecture 


What efficiency is made of
Well-built mobile and web apps are defined by the easy accessibility and stability of their individual components. Thus, creating this skeleton in a manner that allows for both cost and resource efficiency becomes imperative at every step of the way.  In this case, the frontend of the application consists of DAAIP applications and component libraries. The DAAIP unit consists of the following modules:

Authentication Module

Responsible for user authentication and session management 

Alarm Module

Responsible for showcasing alarm details, correlation and summaries

Ticket Module

Responsible for providing network ticket summary in a specific timespan

KPIs Module

Responsible for providing KPI by Vendor-Region-Site-Cells 

AI Studio Module

Responsible for workspace management, data preprocessing and training configuration  

The Application is Divided into Three Major Components

Databases

MongoDB, MySQL, Redis. A cumulation of these three ensures secure information storage, proper reporting of microservice metadata and seamless communication between various tools and services 

Data Engineering

This drives the correlation of alarms, resolutions, time series predictions and anomaly identification. Network outages based on voice call drop, weather conditions and data speed can thus be preemptively predicted

Frontend

Based on an angular framework, this is created using the Google maps library.

“Experience Of How App Development Is Evolving Nowadays,
And What Developers Can Do Better To Ensure A Seamless Frontend And Backend.”

Data Model
The foundation that holds it all together
In any modernized application, the main role of a data model is to support the development of information systems by providing the system
with the correct definition and format of data, such that there is no lag between its inflow and outflow.

The app functions on five data processing pipelines including the following:

KPI Data Processing Pipeline

Responsible for time series prediction of KPI future value and anomaly prediction as per the ML model. It also decides the backend processing for the same 

Ticket Data Processing Pipeline

Responsible for finding out RCA, resolution classification, group classification and problem category classification 

Alarms Data Processing Pipeline

Responsible for pushing alarm data to kafka through the app, finalise on alarms schema, design alarm dependencies and display the results in an alarm dashboard 

CRQ Data Processing Pipeline

Responsible for reading CRQ Data and corresponding the same to input CRQ records 

Inventory Data Processing Pipeline

Responsible for aligning input inventory data table with lookup tables in Redis, design data processing and implementing data processing models

“The Importance Of Having Different Data Libraries In Place, Their Experience In Making This Solution What It Is And Tips For Developers To Keep In Mind For Similar Future Solutions.”

Tech Stack : Building seamlessness within pre existing processes

When creating web applications, developers have a plethora of tech tools, solutioning mechanisms and libraries to choose from. The final decision making needs to rest upon a thorough understanding of the tool, the ways in which they interact with the environment, specific use cases and direct impact areas.

Data Toolset
Open source Big Data Toolset

The frontend interacts with the backend using APIs and HTTP service communication. the backend is designed in a node.js framework, with code level security and continuous code scan processes for catching vulnerabilities at regular intervals.

End Impact

The end result of this activity was a highly efficient singular dashboard for NOC operators, which allowed them to access information related to requests, tickets and status updates visible in real time on a single screen. This not only saves the effort on the operator’s part, but also frees up their time. An easily manageable application infrastructure built on a microservices architecture makes for unparalleled customer experiences. With a promise of end to end delivery, this solution is a holistic approach towards ensuring not just customer satisfaction but customer delight – it has the potential to bring in new business, attract more consumers and ensure seamless delivery throughout.

Revolutionizing NOC Operations  for the  Telecom Industry

Company

Private Company

Domain

Telecom & Networking

Service

Solution Engineering

Technology

Angular, MongoDB, NodeJS, Financial 
Tracker, Mobile, Android, Cloud

Platform

Mobile

Introduction 
The realm of digital has become essential to our daily lives. As the IT industry advances at a breathtaking pace, the need to revolutionise networking as it were is at its peak. We transcended beyond clunky, traditional networks to an age of customer focused solutions, and Network Operation Centres (NOC) have been at the centre of this transformation
To put it simply, the NOC is the central nervous system of the mobile network infrastructure. It is not only the first line of defense against potential threats, but also ensures seamless data inflow and outflow. However, the idea that this is all driven by machines is a misconception, for if the NOC is the brain of the entire operation, then NOC operators are its heart. 
“The pace of evolution in IT, their experience of working in environments with an absence of fully functioning network capabilities”
Problem Statement
Simplifying NOC Operations and network states through automated, deep insight driven AI-ML systems 
Network Operation Centres are hubs of bustling activity when it comes to monitoring transactions, traffic, and user patterns. The NOC operator acts as a liaison between the customer and the network infrastructure to solve any issues related to the above. They are an invaluable resource in the technology puzzle, but are also amongst the most overburdened, owing to inefficient process management, lack of agile working environments and pre existing redundancies
The traditional NOC system involves multiple dashboard screens parallelly in use, with each individual request catered separately. This involves a massive investment of time and effort for the operator, often coupled with slow delivery and issue resolution.
Overview
Multitasking like a Pro:
NOC engineers may find themselves juggling hundreds of requests at the same time. These ticketing requests are often unrelated to each other, and would need immediate resolution. Faults may range from server issues, software bugs or physical defects at the site of the network tower. Thus, the requirement for an automated, agile and intuitive NOC system is increasingly apparent in a digital first era.
The key requirements of a modernized NOC setup include :  

Auto suggestions for ticket key metrics

For reduced human error and analysis of assigned values

Automated alarm correlation and RCA

For faster alarm detection

Digitally managed KPIs and inventory insights

For a holistic view of network corresponding alarms

NOC operators working in a centralized location struggle to keep track of time, incident management and service resolution 24×7, and this is where the need for an end-to-end dissolution model comes in. Here is where the Design thinking approach can make a world of difference. A human centric approach to solving these complex problems not only enables deeper customer engagement but also allows for creative, critical solutions that go beyond just usability.
This is why AI & ML techniques come in handy – to reduce not just the number of tickets created per problem, but also dispatching them on time. Rule mining, problem classification techniques and time series predictions have been able to successfully decrease the time to dispatch for requests.
Keeping this in mind, the iauro team has developed a state of the art application to digitize network operations effectively. With microservices at its core, this application will be used by NOC operators and decision makers to primarily deal with identity management and email servers effectively. In this particular case, the approaches for each problem are tentative and would be finalized post the data analysis for alarms raised, incident management and KPI related requirements.
“Common challenges and network problems from an NOC operator’s perspective, the role of AI and ML in enabling agile, multitask-able environments”
AI-ML Approaches top level view
Cinque Terre Cinque Terre
App and Data Architecture
What efficiency is made of 
Well-built mobile and web apps are defined by the easy accessibility and stability of their individual components. Thus, creating this skeleton in a manner that allows for both cost and resource efficiency becomes imperative at every step of the way.   In this case, the frontend of the application consists of DAAIP applications and component libraries. The DAAIP unit consists of the following modules: 

Authentication Module

Responsible for user authentication and session management 

Alarm Module

Responsible for showcasing alarm details, correlation and summaries

Ticket Module

Responsible for providing network ticket summary in a specific timespan

KPIs Module

Responsible for providing KPI by Vendor-Region-Site-Cells 

AI Studio Module

Responsible for workspace management, data preprocessing and training configuration  

The application is divided into three major components

Databases

MongoDB, MySQL, Redis. A cumulation of these three ensures secure information storage, proper reporting of microservice metadata and seamless communication between various tools and services 

Data Engineering

This drives the correlation of alarms, resolutions, time series predictions and anomaly identification. Network outages based on voice call drop, weather conditions and data speed can thus be preemptively predicted

Frontend

Based on an angular framework, this is created using the Google maps library.

“Experience of how app development is evolving nowadays, and what developers can do better to ensure a seamless frontend and backend.”
Data Model
The foundation that holds it all together
In any modernized application, the main role of a data model is to support the development of information systems by providing the system with the correct definition and format of data, such that there is no lag between its inflow and outflow.

KPI Data Processing Pipeline

Responsible for time series prediction of KPI future value and anomaly prediction as per the ML model. It also decides the backend processing for the same 

Ticket Data Processing Pipeline

Responsible for finding out RCA, resolution classification, group classification and problem category classification 

Alarms Data Processing Pipeline

Responsible for pushing alarm data to kafka through the app, finalise on alarms schema, design alarm dependencies and display the results in an alarm dashboard

CRQ Data Processing Pipeline

Responsible for reading CRQ Data and corresponding the same to input CRQ records 

Inventory Data Processing Pipeline

Responsible for aligning input inventory data table with lookup tables in Redis, design data processing and implementing data processing models

“The importance of having different data libraries in place, their experience in making this solution what it is and tips for developers to keep in mind for similar future solutions.”
Tech Stack : Building seamlessness within pre existing processes
When creating web applications, developers have a plethora of tech tools, solutioning mechanisms and libraries to choose from. The final decision making needs to rest upon a thorough understanding of the tool, the ways in which they interact with the environment, specific use cases and direct impact areas.
Data Toolset
Open source Big Data Toolset
The frontend interacts with the backend using APIs and HTTP service communication. the backend is designed in a node.js framework, with code level security and continuous code scan processes for catching vulnerabilities at regular intervals.
End Impact 

The end result of this activity was a highly efficient singular dashboard for NOC operators, which allowed them to access information related to requests, tickets and status updates visible in real time on a single screen. This not only saves the effort on the operator’s part, but also frees up their time. An easily manageable application infrastructure built on a microservices architecture makes for unparalleled customer experiences. With a promise of end to end delivery, this solution is a holistic approach towards ensuring not just customer satisfaction but customer delight – it has the potential to bring in new business, attract more consumers and ensure seamless delivery throughout.
Taking one liner ideas to make impactful business outcomes.

    Taking one liner ideas to make impactful business outcomes.