An important area in the telecom industry is the network traffic monitoring and analysis (NTMA). Today, Big Data is helping telecom companies to monitor and analyze traffic of huge and complex networks. 

Traditionally networks had a smaller number of nodes which could be easily tracked by network administrators using alarms or assuring correct configuration settings of nodes. 

Now that the amount of data produced by network nodes is more diverse and is growing exponentially, the traditional methods become ineffective in diagnosing problems within the network. In this scenario, big data analytics offers a value driven approach by enabling cost-effective and efficient network monitoring. 

Telecom and Big Data

Over the last two decades, the telecommunication industry has advanced and enabled massive traffic volumes, high transmission speeds, and diverse network services. Alongside the many advantages, there are now more complex monitoring scenarios which are difficult to detect. 

With this complexity in networks, as well as failure of traditional methods to monitor traffic, there lies an opportunity to capture huge amounts of heterogeneous data. This data can then be used to guide various tasks as part of the monitoring and management of the network.

Now before discussing the applications of big data in NTMA, it’s important for your understanding to know what big data is and what are the latest technology requirements for big data analysis.

Five V’s of Big Data 

Big data refers to the data that has storage, and processing challenges of huge volume, velocity, veracity, variety, and value. 

These challenges exist because large data (huge volumes) need to be stored and processed in real time (velocity), that has emerged from diverse data sources that might be structured, unstructured, or semi structured in nature (variety). 

And the data might be accurate or it might lack quality (veracity). Finally, after the analysis, the extracted insights determine the usefulness of the data (value).

Now before discussing the applications of big data in NTMA, it’s important for your understanding to know the latest technology requirements for big data analysis.

Apache Hadoop is a Big Data Platform 

When any dataset conforms to the 5Vs of big data, traditional data analytics systems such as data warehouses fail to process it in an efficient manner. 

Today, telecom networks use distributed computing platforms such as the open source Apache Hadoop which offers a Hadoop instance carrying multiple servers that are distributed across the globe. 

Distributed computing offers telecommunication service providers the ease of running scalable applications with low-cost ownership. 

Under distributed computing, Hadoop infrastructure enables big data analysis by combining a data repository (data lake) with machine learning capability. Features of Hadoop includes: a storage system called Hadoop distributed file systems (HDFS), and machine learning library. 

Spark is another Apache distributed computing platform that is faster than Hadoop because of in-memory design and thus preferred for real-time processing. 

Applications of Big Data in NTMA

Network traffic monitoring and analysis (NTMA) includes applications that range from simple to complex monitoring tasks. These include from comparatively simple overview of network traffic to more complex classification of the traffic based on packet data. 

The purpose is to ensure network services are met and quality of service QoS is maintained.

Traffic Prediction

It’s the estimation of the amount of traffic over a link at a certain time in future. Once it’s known that a link might occupy a large traffic volume in future, then it’s planned to scale up the capacity at the network region to facilitate QoS management. 

Traffic prediction becomes extremely complicated when sudden changes regarding services occur. For example, new services offered as a result of new application deployment over the internet. 

This prediction is made using machine learning models such as deep neural networks that first uses the big data set to train and then predicts accordingly. This big data set essentially carries the historical data of traffic over the same link. 

Traffic Classification

It’s the process of categorizing the traffic (or a network packet) based on the service it belongs to. By knowing the service or protocol, the network can make decisions on fast forwarding the traffic. 

In traditional networking, a port number is used to identify a service which a host wants to utilize. In the TCP/IP protocol suite, which the internet uses today, there are a total of 65,535 ports. 

Given the port number could easily be manipulated, another non-big data technique called Deep Packet Inspection-DPI was introduced. DPI studies a portion of a packet’s content (known as the fingerprint) and matches with a list of known protocols. 

As the data over the network grows exponentially, it becomes increasingly time consuming to match fingerprints. Big data finds its applications in traffic classification through supervised machine learning. 

Instead of matching fingerprints, big data technology uses historical data of packets to learn about services and classifies a packet based on the trained feature set. In unsupervised learning packets are classified without the availability of training data.

Another concern in traffic classification is the compliance to security procedures making it a difficult task. While it’s not possible to decrypt packets along the transmission journey, big data adopts the approach of a behavioral method of classification. 

In behavioral classification, the packets are classified based on parameters such as data rate, packet size, server from which traffic originates, and the series of servers the traffic passes through.

Fault Management

Fault management is the process of predicting, detecting, isolating, and fixing the fault in the network. Its purpose is to reduce the downtime making the network more resilient. 

There are two kinds of fault management, proactive, and reactive. In a proactive approach, faults are predicted and network paths are planned to avoid disruptions. In reactive approach, fault is detected only once it has occured. 

Today, as the networks have become increasingly large, tracking faults such as hardware failure, connectivity loss or port status change through ICMP ping or SNMP is a challenge. 

In case of reactive fault management, big data analytics tracks faults through analysis of traffic and system logs. Take example of a critical router failure that blocks a route forcing traffic towards other routes thereby overloading the network. 

The problem of a device failure may also give rise to a chain of problems in the network. For example, certain nodes become inaccessible from a critical node becoming faulty.

Such a problem requires real time processing of available data to track the issue. Big data technology comes in handy here. 

The diverse network nodes will generate system logs related to the ongoing failure of the router and the rerouted traffic handled by neighboring routers. This data along with traffic parameters and other measurements creates a huge and diverse data set which is now analyzed in real time to track faults.  

There’s much more!

Applications of Big Data analytics are not limited to the above three areas and also include other NTMA functions such as network security, congestion control, traffic routing, resource management, and QoS management. However, research is not mature yet for these areas and need more observation and surveying.

Telecommunication industry will see excessive use of Big Data analytics across its various functions by the end of 2027. This is because Big Data Industry is expected to reach to its all time high market share worth $450bn in next five years. Investments in AI focused startups is already increasing and the role of human talent is considered more important to support the promise of Big Data.

With this massive advancement, companies across various industries will be looking for worthy talent so there’s bright opportunity for you to start your career in analytics. If this career choice sparked in you, then our experts at Dice Analytics can help with free career counseling sessions. You can also reach to us and foster your analytics talent through our well curated and industry known certification programs.