Multimodal Observability for Input Output Bottleneck Detection

Abstract

Modern distributed data pipelines increasingly rely on complex combinations of storage systems, message queues, compute frameworks, and networked services to process large volumes of data. Within these pipelines, Input/Output bottlenecks remain a persistent and difficult challenge, often manifesting as reduced throughput, increased latency, and unpredictable performance behavior. Existing observability approaches typically monitor Input/Output activity using isolated metrics such as disk utilization, network throughput, or queue depth. While these metrics provide partial visibility, they are often insufficient to accurately localize bottlenecks in distributed environments where performance degradation arises from interactions across multiple layers of the system. As a result, operators frequently face delayed diagnosis, misattribution of root causes, and inefficient mitigation strategies. Current monitoring systems predominantly rely on single-modal observability data, focusing on either metrics, logs, or traces in isolation. This fragmented visibility limits the ability to correlate low-level Input/Output events with higher-level pipeline behavior, especially under dynamic workloads and variable access patterns. These limitations become more pronounced as pipelines scale across heterogeneous infrastructure, where Input/Output contention may shift between storage, network, and application layers over time. This paper proposes a Multimodal Observability framework for Input/Output bottleneck detection in distributed pipelines. The framework is designed to integrate metrics, logs, and traces into a unified observability model, enabling cross-layer correlation of Input/Output behavior. By aligning low-level Input/Output signals with execution paths and system events, the proposed approach aims to improve the precision of bottleneck identification without relying on isolated indicators. The framework focuses on systematically capturing and correlating Input/Output interactions across pipeline stages to distinguish true bottlenecks from secondary performance symptoms. Through this approach, the paper seeks to address the limitations of existing observability practices and establish a structured methodology for detecting and analyzing Input/Output bottlenecks in complex distributed data pipelines.