Evaluation of Adaptive Micro-batching Techniques for GPU-Accelerated Stream Processing

Stream processing plays a vital role in applications that require continuous, low-latency data processing. Thanks to their extensive parallel processing capabilities and relatively low cost, GPUs are well-suited to scenarios where such applications require substantial computational resources. However, micro-batching becomes essential for efficient GPU computation within stream processing systems. However, finding appropriate batch sizes to maintain an adequate level of service is often challenging, particularly in cases where applications experience fluctuations in input rate and workload. Addressing this challenge requires adjusting the optimal batch size at runtime. This study proposes a methodology for evaluating different self-adaptive micro-batching strategies in a real-world complex streaming application used as a benchmark.