Adaptive Traversal Rate Optimization for Large-Scale Content Repository Indexing: A Feedback-Controlled Approach to Balancing Search Freshness and Source System Stability
DOI:
https://doi.org/10.22399/ijcesen.5305Keywords:
Enterprise Search, Adaptive Crawl Rate, Feedback Control, Traversal Optimization, Freshness-Impact RatioAbstract
Enterprise search systems must continuously index content from heterogeneous source repositories—including collaboration platforms, relational databases, and content management systems—to maintain search result freshness, yet aggressive crawling induces performance degradation on source systems serving concurrent production workloads. This paper formalizes the content traversal problem as a feedback control optimization, where document ingestion rate constitutes the control variable, source system response latency and error rates form the observed plant output, and aggregate search staleness serves as the optimization target. We introduce the Adaptive Traversal Rate Controller (ATRC), a throttling algorithm inspired by proportional-integral-derivative (PID) control, which includes heterogeneous-repository-aware gain scheduling, incremental change detection using checkpoint-based delta computation, and Access Control List (ACL)–aware traversal prioritization. ATRC dynamically adjusts per-connector crawl rates based on real-time source system health telemetry, achieving Pareto-optimal trade-offs between search freshness and source system impact. We further introduce the Freshness-Impact Ratio (FIR), a standardized dimensionless evaluation metric enabling cross-platform connector benchmarking. Experimental evaluation on three production-representative connector types—SharePoint (N = 12.4M documents), JDBC-accessed relational databases (N = 47.8M rows), and hierarchical content management systems (N = 8.2M assets)—shows that ATRC achieves 41.3% better freshness than fixed-rate crawling, reduces source system CPU overhead by 26.7%, and outperforms reactive backoff heuristics by 18.4% in FIR. Longitudinal validation over 24 months of production operation across repositories serving more than 200 million users confirms the framework's stability under non-stationary workload conditions.
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