Data-Driven Network Operations: Framework for Telemetry, Analytics, and Automated Remediation

Abstract

The transition to data-driven decision-making, particularly when it comes to contemporary network operations, can not be achieved without an efficient form of automation, yet to approach it, it is required that more than high-volume data collection by telemetry is involved. This paper outlines a design of reliable network operations that includes telemetry ingestion, data quality management, temporal modeling and topology awareness, analytics and automated remediation as a part of a controlled data lifecycle. The architecture suggests a canonical event envelope to offer consistent identity, event time, semantics and provenance to heterogeneous sources. Topology model and temporal entity assist in giving operational context and purpose-specific data store and controlled transformation pipeline are useful in assisting assurance, capacity planning, anomaly detection and remediation processes. A feature registry makes reusable analytical representations and the evidence provenance keeps track of the provenance between the source observations, transformations, analytical outputs and operational actions. The framework specifically addresses the problem of incomplete, late, repetitive, and semantically incoherent information by incorporating measures of quality and decision processes that are conscious of the confidence. Framework assessment is a framework appraisal that gauges completeness, freshness, semantic consistency, lineage coverage, reproducibility, and downstream quality of choice. Based on the proposed architecture, credible automated remediation is achievable, as indicated by the capabilities to reuse the evidence of all decisions to improve the transparency, operational soundness and regulated reuse of network data throughout the transforming analytic and automation processes.