A High-Throughput Distributed Job Queue Architecture: Optimizing Concurrency, MultiPriority Routing, and Fault Tolerance Utilizing Redis-Backed Asynchronous Workers

Abstract

Modern cloud microservice ecosystems increasingly rely on asynchronous task execution to prevent synchronous blocking, thread starvation, and cascading latency degradation across decoupled services. However, designing a high-throughput, fault-tolerant job dispatch and processing subsystem requires balancing in-memory broker efficiency, strict multi-priority scheduling, distributed rate limiting, and dual-layer state persistence. This paper introduces the design, mathematical modeling, algorithmic implementation, and theoretical performance analysis of an enterprise-grade distributed job queue system. Utilizing an in-memory Redis data-structure store mediated by the BullMQ queue engine, coupled with a Node.js asynchronous event-driven runtime and MongoDB audit persistence, the architecture establishes an elastic worker pool operating at 25+ concurrent task threads across specialized domain queues (email, processing, data-sync, report, and default).