The objective for modern Site Reliability Engineering (SRE) within telecommunications, such as T-Mobile, is to transcend reactive fault detection (e.g., post-event log analysis) toward predictive feature spotting. This means identifying subtle, pre-fault operational patterns days or weeks before a network failure occurs. Achieving this requires an industrialized,
carrier-grade data platform—a complete AIOps Chassis.
BaseN provides this foundation. Operating as a mature Platform as a Service (PaaS) and Software as a Service (SaaS) solution since 2001, the platform is engineered to capture, process, and control extreme volumes of correlated timeseries data in real-time. BaseN serves as a standardized platform for Digital Twins, inherently integrating core SRE functions, including Fault Management, Performance Monitoring, and Root Cause Analysis (RCA).
Unifying the SRE Data Fabric for Predictive Readiness
Successful AIOps requires eliminating the prevalent challenge of data fragmentation—where siloed monitoring tools result in missing context and slow troubleshooting. BaseN’s architecture addresses this by creating a single, contextual data fabric, essential for accurate predictive modeling.
Holistic Data Ingestion and Contextualization
The BaseN platform is designed for multi-modal telemetry ingestion, collecting and correlating data from diverse systems, including cloud platforms, on-premises infrastructure, and network monitoring tools.
- Extreme Real-Time Capacity: BaseN processes and controls extreme volumes of operational data in real-time, functioning as a complete Digital Twin environment.
- Deep Network Intelligence: Beyond traditional metrics and logs, the platform includes native Traffic Analysis supporting deep flow protocols such as Cisco NetFlow, jFlow, sFlow, and IPFIX. This is vital for predictive analysis. As abnormal traffic patterns, often missed by reactive log systems—signal impending performance degradation.
- Correlation and RCA: The architecture is built to unify disparate datasources via Complete APIs and native correlation features. It allows for a holistic view of IT operations. By correlating events and identifying dependencies across systems before the data is passed to the AI engine, BaseN ensures the feature set used for ML training is clean, contextualized, and primed for advanced analysis.
BaseN’s most potent value lies in its capability to rapidly inject both machine learning algorithms and scalable human domain knowledge directly into the real-time data flow. It basically accelerate the path to prefault identification. BaseN facilitates the critical process of operationalizing human expertise:
1. Expert Intelligence Capture: After a human expert investigates a root problem using the platform’s tools and data availability, their intelligence (e.g., a specific sequence of counter anomalies) can be quickly and easily codified into automated logic.
2. Scaled Automation: This high-quality, expert-validated logic can be used to train and refine AI models. It significantly accelerate any model development. BaseN ensures that any successful prototype can scale easily to any size across the network via multilayer programmatic templates and scripts, guaranteeing CI/CD repeatability.
3. Quick Logic Deployment: This human-derived logic is rapidly deployed via programmatic templates and scripts directly into the ongoing data fabric. It ensures that the problem pattern is spotted upfront the next time it occurs. It generally act as an immediate, conventional backup solution while complex AI model refinement is underway.
