How will AI and ML influence Telco CSPs in Service Management Domain ?

Artificial Intelligence (AI) and Machine Learning (ML) are two sizzling buzzwords at present.

What is Machine Learning ?
As per Wiki, Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed


Following are the different types of ML available today:

• Supervised learning
• Clustering
• Dimensionality reduction
• Structured prediction
• Anomaly detection
• Neural nets
• Reinforcement learning

What are the major domains where Telco CSPs shall apply ML and AI ?

• Service Management
• Network Management

Of course, it is not limited to only these two, my focus in only on OSS and NMS applications here.







ML Algorithms usage in OSS/NMS :

Here, we will see some scenarios where ML/AI can be used in Fault, Configuration and Performance Management models.

UnSupervised Learning : Using UnSupervised Learning – Anomaly detection, we can detect Faults in the Telecom Network.

As per Wiki, In data mining , anomaly detection (also outlier detection ) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset .

Fuzzy logic based outlier detection can be one of the best technique for anomaly detection. Telco OSS/NMS vendor can create a rule-set in their application with a logic when Software detects a critical alarm from an EMS/NE it shall follow an automatic work flow and automatically learn that some fault has occurred and can be asked for a correction. It can be done in many ways. One such example is: Open a trouble ticket in your Service Management application and take steps to perform corrective actions based on previous changes made when a fault occurred.

In Performance Management model, ML can be of extensive advantage for a CSP operator who is sitting in NOC.

Historical Performance Management Data (Raw/Processed Counters) shall be stored in Big Data platform. ML Algorithms shall perform analysis and predict the future Performance/Capacity of the Network/Network Element.

Forecasted Performance analysis shall be automatically fed into Sophisticated Reports for customers to have a view. Based on analysis results, system can automatically take decisions based on optimizing the capacity so that it shall not give any trouble to the Mobile users using the network.

Ok, but do you think is it good to allow the system to take a decision in optimizing the network which may involve a human approval ? 

Aaahn Sometimes NO !!! In this case, we go for Supervised learning.

As per Wiki, Supervised Learning is the machine learning task of inferring a function from labeled training data. 

Training data for a OSS/NMS is nothing but the FCAPS and Service Management data stored in OSS/NMS Database. Results of Supervised Learning shall pop-up a report to an operator which asks for permission/confirmation to change a network parameter (Configuration Management).

With little intervention of human approving the change, ML algorithms shall tune the Config Parameters. Again, tuning of Configuration Parameter can also be made dependent on ML Algos say for eg: Clustering

As per Wiki , Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster ) are more similar (in some sense) to each other than to those in other groups (clusters). 

A group of Radio/Core Elements can be clustered together say for a particular REGION and Clustering Analysis shall be applied.

What is the primary benefit of using ML/AI in OSS/NMS ?
Our ultimate aim should be “Analyze the data flowing to your NMS/OSS from your Network elements and make forecast investigation what is going to flop and help the operator to avoid the fault”.
Obviously, this helps in giving CSPs to achieve a super cool Customer Experience Management !!