Adaptive Priority Arbitration for Stateful Distributed Pipelines

Modern distributed pipelines frequently process continuous streams of updates that contribute to evolving shared state across multiple system components. Examples include telemetry processing platforms, distributed sensor fusion systems, real-time analytics pipelines, and online inference services. In these environments, resource arbitration decisions influence not only throughput but also state freshness, latency variance, and the correctness of downstream computations. Traditional scheduling approaches such as FIFO, static-priority queues, and fair-share policies are often insufficient because they treat jobs as independent tasks rather than components of stateful distributed workflows with ordering constraints and latency-sensitive updates. This paper examines resource arbitration as a distinct systems problem in stateful distributed pipelines. We introduce an adaptive priority arbitration framework that combines static importance with runtime signals including mean latency, latency variance, backlog growth, and state freshness. The framework also incorporates inheritance-style priority escalation to mitigate cross-stage priority inversion in multi-stage pipelines. Rather than advocating a single scheduling rule, we define a policy taxonomy that includes ordered, latency-sensitive, variance-sensitive, inheritance-based, and fair-share arbitration models. We evaluate the proposed approach using a simulation-based framework that compares adaptive arbitration policies against traditional scheduling baselines under mixed-priority and bursty workloads. Our results show that adaptive policies incorporating latency and freshness signals substantially reduce tail latency while maintaining balanced throughput and preserving ordering guarantees, suggesting that adaptive arbitration provides a practical alternative to rigid scheduling policies in stateful distributed systems.