Semantic Condensation of High-Cardinality Time Series for LLM-Driven Observability - Akila Balasubramanian (Malikussaleh University, Aceh, Indonesia, 2026)
Modern cloud observability platforms generate high-cardinality time-series data comprising thousands of distinct metric streams differentiated by service, endpoint, region, infrastructure component, and operational context. While this telemetry provides valuable insights into system behavior, its volume and complexity are inherently mismatched with the reasoning constraints of Large Language Models (LLMs), which operate within finite context windows and token budgets. When raw telemetry is passed directly to LLM-based observability assistants, four recurring failure modes significantly degrade analytical quality: token explosion that saturates context windows before signal-rich entries are reached, lossy truncation that discards diagnostically critical streams under arbitrary cardinality limits, incoherent analytical narratives arising from contradictory statistical summaries, and resolution gaps that obscure trend evolution, anomaly propagation, and change-point detection. These limitations reduce the effectiveness of LLM-assisted troubleshooting and hinder the practical adoption of AI-driven observability systems in large-scale cloud environments. To address these challenges, this paper introduces Semantic Condensation, a token-aware transformation framework designed to convert large-scale time-series telemetry into structured, semantically consistent summaries optimized for LLM-based reasoning. The proposed approach integrates vectorized statistical pre-analysis to efficiently identify anomalous behaviors, multi-signal importance scoring to rank Time Series Identifiers (TSIDs) according to diagnostic relevance, behavior-aware trend classification to capture temporal dynamics, and consistency enforcement mechanisms to prevent contradictory interpretations across generated summaries. Furthermore, an adaptive token-budget allocation strategy dynamically distributes descriptive detail based on diagnostic importance, ensuring that the most critical telemetry receives greater representational fidelity while maintaining strict token efficiency.Experimental evaluation conducted on high-cardinality observability workloads demonstrates that Semantic Condensation can process more than 10,000 time-series identifiers within production-grade latency requirements. Results show substantial reductions in token consumption compared with raw telemetry transmission, while simultaneously improving downstream LLM reasoning accuracy, anomaly interpretation, root-cause investigation, and troubleshooting effectiveness. These findings indicate that Semantic Condensation provides a scalable and practical foundation for next-generation AI-assisted cloud observability and operational intelligence systems.