Modern technology has resulted in significant changes to network and infrastructure. When a network problem occurs, the consequences can be disastrous and difficult to resolve.
With a number of reasons which can lead to network faults, firms should be alert and aware of the issues that may bound to happen. According to Gartner, the cost of unplanned network downtime is around $5,600 per minute.
Firms can avoid critical issues before they take place by being a step ahead by solving network faults. This will lead to saving much of human time and money spent after a mishap.
Solving network faults at the earliest provides numerous benefits, including continuous network availability to users, reduced downtime and so on.
Different ways to solve Network faults
Root Cause Analysis to avoid Communication Service Disruption
KT(Korea Telecom) developed its own network failure Root Cause Analysis (RCA) solution called ‘Dr Lauren’.
Dr Lauren collects alert data from network operations, analyzes it using an AI algorithm, and automatically identifies the cause and location of network failures within a minute. Dr Lauren’s main strength is that it combines KT’s network management service expertise with AI to ensure analysis accuracy for identifying and locating failure causes.
Furthermore, Dr Lauren can benefit the operator by increasing customer satisfaction as a result of a faster network failure response, retaining customers and improving overall brand image. By automating network operations and monitoring, Dr Lauren also enables the operator to provide stable services outside of standard working hours.
Predictive Network Engine to forecast network issues
Predictive networks can forecast events based on past events by analyzing massive amounts of historical data.
AI and ML capabilities introduced Cisco Predictive Networks to learn by aggregating data from a wide range of services. The AI and ML technology used by the Predictive Network produces high accuracy for an optimal experience.
Concerning the type of networking data that the predictive engine can process, a Cisco spokesperson stated that there are “no blind spots or limitations” in terms of what it can see and monitor. Cisco states that it could monitor telemetry data from applications, traffic volumes, log events, and topology.
Anomaly Detection to identify network behavior
A large number of log messages were generated from various router types. The current procedure of the client involved manually going through millions of log messages to identify router network faults. Obtaining useful insights from it to analyze network behavior had become a difficult task for the client.
GeakMinds approach was to ingest live streaming log from on-premises sources into Azure Data Explorer (ADX). To detect anomalies in the time series data, the ADX built-in anomaly detection model was used. To accomplish this, the model employs the seasonal decomposition method.
The seasonality and trends in the data were determined by looking at counts from the previous 24 hours.
AI-driven Operations (AIOps) to identify source of network issues
With real-time anomaly detection and event correlation to pinpoint the source of problems, AI can simplify network operations. It can also recommend timely remedial measures to correct problems before anyone notices them.
AI has enabled the development of new virtual network assistants capable of answering network questions on par with domain experts. IT teams can ask normal questions, such as “What was flawed with Mike’s Zoom call last Friday?” and the system will respond with specific insights and recommendations based on observations made across the LAN, WLAN, and WAN by leveraging natural language processing (NLP) and natural language understanding (NLU).
Cell Site Degradation Prediction to predict network degradations
Nokia AVA AI solutions perform diagnostic and predictive analysis on a variety of data to forecast future network KPI performance. This enables CSPs to anticipate network degradations and deal with problems before they occur, improving service quality and reducing customer complaints.
Cell Site degradation prediction enables CSPs to detect network incidents in advance and take corrective actions to ensure continuous network service delivery.
The Nokia AVA Cell site degradation prediction use case assists engineers in predicting which 20 sites’ KPIs are most likely to degrade in the next seven days, allowing them to be corrected to avoid network degradation, improve service quality, and reduce customer complaints.
The AI prioritizes incidents on high-value sites, allowing teams to focus on what is most important to the customer experience.
Conclusion
The network is one of the key areas to be focused on by any telecom firm. It eventually helps firms maintain a steady user base and remain at the top among the competitors. Telecom firms should take necessary remedial steps and apply technologies to solve their network issues at the earliest. To know more and for assistance to guide you to the right solution, reach out to geakminds.com.