KATANA IPM Analytics

Leveraging advanced analytics and AI engine, IPM predicts and prevents network performance issues before they happen, building Capacity growth models and forecasting user behaviour and traffic load on the network, giving proper recommendations that keep network performance on track with this growth and user behaviour changes.

Figure 1: KATANA Platform Modules

IPM Analytics is the heart of KATANA IPM module, and it offers the below different uses cases.

Figure 2: IPM Main Functionalities sub-modules

Anomaly Detection:employs a machine-learning algorithm to understand the patterns of Key Performance Indicators (KPIs), making comparisons and autonomously recognizing deviations. The resulting scores are presented for each instance, facilitating straightforward identification of anomalies and their deviation from the typical cluster or common behavioral patterns within the network.

Forecasting Analysis: iPM encompasses various forecasting techniques within a unified interface, granting users the capability to analyze anticipated future trends in network usage for any counter and Key Performance Indicator (KPI).

Figure 3: DL Traffic Volume Forecast

Capacity Management: As networks expand and experience heightened traffic, there is often a decline in network performance. To prevent this deterioration, iPM Capacity management function becomes crucial to enhance performance and restore it to its initial levels. iPM is Addressing traffic shifts requires the strategic rebalancing of network traffic, ensuring even utilization across the network, thereby deferring capital expenditures on new equipment.

Worst Cell List: The Worst Cell List Report, an integral component of our iPM capabilities, is robustly supported by ranking conditions tied to specific periods for designated Key Performance Indicators (KPIs). This functionality empowers users to assess nodes with the poorest performance through detailed maps and charts.

Worst Degraded List: This module, seamlessly integrated into our iPM suite, efficiently troubleshoots and compiles a list of nodes with degraded performance over a specified period. It conducts in-depth analyses through maps and charts, facilitating immediate examination at the work area for detailed troubleshooting

Figure 4: Creation Criteria for WDL

Benchmark: After implementing an optimization action, users have the flexibility to initiate a benchmark across a set of Key Performance Indicators (KPIs). This benchmarking can be conducted on a Day-to-Day, Week-to-Week, or Month-to-Month basis, allowing for comprehensive performance evaluation over various timeframes.

Swap & Acceptance: In Swap Projects, users are required to compare Key Performance Indicators (KPIs) before and after the swap. iPM provides users with the convenient option to effortlessly compare the performance of vendors, facilitating a streamlined assessment of the impact of the swap on network performance.

Eagle Eye: Revolutionizing Mobile Network Testing with INOS

Introduction

In the world of mobile network testing, efficiency and accuracy are crucial for optimizing network performance. The “Eagle Eye” feature in INOS is a powerful tool that enables users to analyze large data sets in logfiles, extracting valuable insights through geofencing. This feature facilitates data-driven decision-making for network optimization.

Unveiling “Eagle Eye”

The “Eagle Eye” feature in INOS allows users to effortlessly search through logfiles containing millions of samples, making it a reality. Unlike the traditional method that required meticulous effort in recalling file names, dates, and locations, “Eagle Eye” offers an intuitive solution. Users can define their area of interest using geofencing, and the feature retrieves all relevant data within that area, saving both time and effort.

Figure 1

Optimizing Search Results

“Eagle Eye” offers various settings to streamline the search process, allowing users to optimize and narrow down their results efficiently. These settings include:

  • Time Aggregation: Users can pick time granularity (hourly, weekly, or monthly) to analyze data over specific periods, aiding trend identification.
  • Distance Aggregation: Users can set the desired distance aggregation for a detailed location-based network performance analysis.
  • Operator MNC/MCC: Users can filter their search results based on specific Mobile Network Code (MNC) and Mobile Country Code (MCC).
  • KPI Selection: Users can choose the Key Performance Indicators (KPIs) they wish to extract from the logfiles.
  • Date Range and Time Clustering: Users can set a date range and cluster data in time to better understand network performance changes in specific periods.

Interpreting the Results

Once the search parameters are set, “Eagle Eye” presents users with an interactive map accompanied by a timeline.

Figure-2

“Eagle Eye” offers a user-friendly visual representation for easy navigation, providing insights into network performance across locations and timeframes. It also includes benchmark tables and histogram charts for comparative analysis and trend identification.

Use Case Scenarios

The application of “Eagle Eye” in INOS extends to various use cases like;

  • Pre-action Network Assessment
  • Performance Benchmarking
  • Team Performance Assessment
  • Investment Impact analysis
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Conclusion

“Eagle Eye” in INOS is a game-changer for mobile network testing, with geofencing, result optimization, and visual representations that empower efficient insights extraction. It enhances decision-making, network performance, and operational excellence in mobile network testing.