A duplication based algorithm for optimizing latency under throughput constraints for streaming workflows

Scheduling, in many application domains, involves the optimization of multiple performance metrics. For example, application workflows with real-time constraints have strict throughput requirements and also desire a low latency or response time. In this paper, we present a novel algorithm for the scheduling of workflows that act on a stream of input data. Our algorithm focuses on the two performance metrics: latency and throughput, and minimizes the latency of workflows while satisfying strict throughput requirements. We leverage pipelined, task and data parallelism in a coordinated manner to meet these objectives and investigate the benefit of task duplication in alleviating communication overheads in the pipelined schedule for different workflow characteristics. The proposed algorithm is designed for a realistic k-port communication model, where each processor can simultaneously communicate with at most k distinct processors. Evaluation using synthetic and application benchmarks shows that our algorithm consistently produces lower-latency schedules and meets throughput requirements, even when previously proposed schemes fail.