Understanding Internet Outage Detection: Batch vs. Streaming Trinocular Systems

Internet outage detection plays a critical role in understanding and improving network reliability across the globe. Among the various tools and algorithms used in this domain, the Trinocular system has gained popularity for its effectiveness in identifying outages. Originally based on batch processing, the Trinocular system was redeployed in 2016 to include a near-real-time streaming capability. This update allows for more immediate analysis compared to the traditional three-month batch cycles, making it a widely adopted solution for both commercial and academic studies.

The Evolution of Trinocular: Batch vs. Streaming

The key distinction between the two versions of Trinocular lies in their operational methodologies. While the batch system processes extensive data sets over a long period, the streaming system delivers results in near real-time by continuously analyzing incoming data. Recent research by Dr. John Heidemann, Yuri Pradkin, and Erica Stutz sought to compare the performance of these two approaches. Their analysis, conducted over an eight-day period, revealed that the batch and streaming versions agreed more than 84% of the time. This strong correlation validates the reliability of both methods, yet differences in reporting patterns highlight their unique applications.

For example, in cases of extended outages, the streaming system tends to overreport certain events, possibly due to limited data availability or inconclusive reachability verification. On the other hand, batch processing offers a more accurate but time-intensive approach, making it ideal for high-accuracy applications where delays are acceptable. These findings underline the complementary nature of the two systems, where streaming provides quick updates and batch ensures rigorous validation.

Real-World Use Cases and Observations

The study analyzed specific network events to illustrate discrepancies between the two systems. One such example occurred on March 2, 2021, in the G7 Telecom Ltd network in Bahia, Brazil. Two five-hour outages, separated by a brief period of connectivity, impacted 23 IP blocks across multiple prefixes. The streaming system effectively highlighted these downtime events, though its propensity to overreport became evident in extended outage cases.

Another outage, detected in Seoul, South Korea, also on March 2, 2021, affected 27 IP blocks within the LG POWERCOMM network. This four-hour outage, while well-described by both batch and streaming systems, underscored differences in granular detection. Streaming identified more intermittent outages compared to batch processing, showcasing its strength in capturing real-time fluctuations but also revealing biases in prolonged event reporting.

Implications for Outage Detection and Future Research

The implications of this research extend beyond academic curiosity. Network operators and technology companies can leverage batch processing for thorough post-event analyses while relying on streaming detection for rapid response to ongoing issues. This dual strategy ensures a robust approach to outage identification and resolution. Moreover, the importance of validating algorithms through independent implementations, even when they share foundational concepts, cannot be overstated. Such validation builds trust in network reliability tools and drives innovation in this ever-evolving field.

This study, initiated by Erica Stutz during her time at Swarthmore College and continued with Dr. Heidemann and Yuri Pradkin at the University of Southern California, exemplifies the value of cross-disciplinary collaboration. It also highlights the ongoing role of researchers and institutions in enhancing global network performance and resiliency.