Distributed Execution Models for Long-Running Tasks
Long-running computational tasks, ranging from batch data processing to multi-day simulations, present operational challenges that differ fundamentally from request-response workloads. They cannot be served by standard autoscaling groups, they do not fit neatly into container orchestration patterns designed for short-lived processes, and they demand different approaches to fault tolerance, resource allocation, and cost management. This paper examines three distributed execution models that have proven effective for long-running workloads: work queue architectures, actor-based processing, and stateful stream processing. For each model, we document the operational characteristics, the failure modes, and the conditions under which the model outperforms alternatives. We find that the choice of execution model should be driven by task characteristics, namely statefulness, idempotency, and duration, rather than by organizational familiarity or available tooling.