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RTT analysis with Cubro Omnia MA - precise measurement of service response time

New generation of Call Detail Record using Cubro Omnia QM platform

Cubro 's solution includes an advanced measurement technique that calculates the circulation time of data packets, particularly from the service provider's network to the subscriber's device and back again. The result is a more precise assessment of network performance, facilitating improved optimization strategies.

Much of user satisfaction depends on service response time. Knowing the latency or round-trip time information of each subscriber is critical to improving customer service. However, providers often have no control over web services, which account for more than 70% of total session latency. Poor round-trip time of a session can have two causes: on the user side (internal delay) or on the application side (external delay). Conventional measurement methods always mix internal and external latency values, resulting in misleading KPIs.

Cubro's innovation in calculating internal Round-Trip time

Cubro goes one step further by performing internal round-trip timing, accurately calculating the time required for a data packet to travel from the service provider's network to the subscriber's end device and back again. This innovative mechanism effectively eliminates the impact of unpredictable service response times associated with the World Wide Web, offering a true measure of internal Key Performance Indicator (KPI) latency. This advanced approach provides a more accurate assessment of network performance, contributing to improved optimization strategies.

How the Cubro Omnia QM solution works

Benefits of the Omnia MA

The KPI can be correlated with several other network indicators, such as eNodeB coverage data and performance values, to determine whether poor round-trip transfer values of several subscribers are caused by an overloaded base station. The KPI can be grouped by service, such as speed testing tools, to assess whether dissatisfied customers are performing more speed tests when they have poor latency.

  • Holistic Network Performance Assessment: By correlating KPIs with various network metrics, such as eNodeB coverage data and performance values, the solution offers a holistic view of the network.
  • Service-specific analysis: Grouping KPIs by services, such as speed testing tools, provides service-specific analysis. This can help you understand user behavior and preferences under different network conditions.
  • Identification of overloaded base stations: The solution allows you to identify potential problems with overloaded base stations. This is critical for network optimization as it allows operators to address and address bandwidth issues that can impact the user experience.
  • Control over User Experience: The ability to correlate KPIs with user experience metrics allows for effective customer experience management. Operators can proactively address issues affecting subscribers, leading to improved satisfaction and loyalty.
  • Data-backed decision-making processes: The solution enables operators to make informed, data-driven decisions. By analyzing KPIs along with other relevant metrics, operators can prioritize network optimization activities, allocate resources efficiently, and implement targeted improvements.
  • Proactive troubleshooting: With the insights provided by the solution, operators can take proactive action to resolve issues before they have a significant impact on subscribers. This contributes to more stable and reliable network performance.