Know How Data Science Can Boost Network Operations

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Know How Data Science Can Boost Network Operations

Siddharth K M

@siddharthkm

September 19, 2022

Data Science & AI Community

118

Know How Data Science Can Boost Network Operations

Introduction to Data Science

The use of data science methods in areas like fraud detection, customized recommendations, etc., has been effective for many years. Service providers and telecom network operations have recently used these strategies. Making networks more dependable and safe is becoming easier with the help of SDN/NFV and data science.

Data Science Explained

Automated techniques are used in data science to analyze vast volumes of data and derive information from it. Statistics, computer science, applied mathematics, machine learning/artificial intelligence, and visualization are all parts of the broad field of data science.

Email spam filters are one of the common uses of machine learning. Millions of emails that have been pre-classified as spam or not are processed to train the algorithms. The result is a program that recognizes the great majority of junk emails automatically and continually enhances and modifies itself as new samples become available.

Relevance to SP network operations

The underlying network architecture has grown increasingly complicated and dispersed as SPs implement SDN/NFV. The dynamic network presents SP operations teams with unparalleled change, size, and complexity. It is difficult to predict and predict what will go wrong in this dynamic SP network environment. The manual procedures and human correlation processes used for many years are no longer efficient.

Data science has the potential to revolutionize SP network operations, including the elimination of the need for human network optimization, monitoring, and troubleshooting. The key is figuring out how to do it in a way that offers obvious business value, is integrated into the SP operations cycle, and makes use of both data and expert knowledge.

Reducing alert fatigue

Compared to legacy networks, the number of components that need to be monitored and managed has exponentially increased in the new world of SDN/NFV. The deluge of data from dispersed network components that produce logs and warnings is one of the biggest issues SP operations teams are now experiencing.

Operations teams cannot concentrate on what is important due to a low level of prioritization and a high false-positive rate. In order to create a prioritized list of alerts for the SP operations team to evaluate and respond to, data science techniques may be used to comprehend the context of the warnings and suppress the ones that are not pertinent.

Proactive network optimization

Data science offers a mechanism for fast analyzing massive amounts of monitoring data produced by network devices, identifying recurring patterns in their behavior, and creating precise models of their performance. The automatic detection of system behavior anomalies that can be related to network problems can be done using anomaly detection techniques. Simple example: If there have been three standard deviations more link faults on one network interface in the past ten minutes than on other links in the same network, this may be cause for concern.

Advanced security

Traditional security methods rely on rules and signatures, which can only detect attacks using outdated data. The number of sophisticated and unidentified attacks aimed at SP networks grows as attackers' strategies change quickly.

Algorithms may be trained to understand the SP environment and adapt to the danger landscape, making judgments about if anything is malicious and then giving the expert context to aid in a speedy investigation.

Future of SP operations

Self-driving cars offer crucial information about the direction that data-driven automation is likely to go. The realm of SP network operations may be expanded using the general ideas employed in self-driving automobiles.

Collecting enormous volumes of data, letting computers carry out repetitive activities, and putting in place self-learning systems that can adjust to unforeseen circumstances, The result will probably be intelligent network management software capable of reliably carrying out numerous SP operational duties.

Conclusion

Self-healing is already used by several hyper-scale operators (Facebook, LinkedIn, Netflix, etc.) for some fundamental operational duties. SP operations must transition to "management by exception" in the future, where the majority of faults and performance degradations are fixed by automatic self-healing. Data Science should be used everywhere to improve any organization's productivity and profitability.

Data Science data science course network operations

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