StreamBucket: In-Network Adaptation for Late-Binding Stream Processing Systems

Stream processing applications are increasingly de-ployed on hierarchical geo-distributed edge-cloud environments. Applications in such environments require frequent deployment reconfigurations to balance performance and cost by optimally utilizing the diverse resources. Recently, late-binding stream processing frameworks using a hierarchical network of routers have been introduced to seamlessly execute such reconfigurations with minimal impact on application performance. However, late-binding frameworks face scalability challenges as the routers can become a bottleneck. This restricts the maximum throughput that can be achieved by the network. We propose StreamBucket, a novel protocol that increases the capacity of late-binding routers by intelligently batching the tuples at various levels of the routing network and employs header compression techniques to reduce bandwidth. We design a model to predict the optimal batch size for unseen workloads, which reduces the amount of costly profiling. Our evaluations show that StreamBucket achieves up to 5x improvement in throughput using multi-tier batching and between 15% to 85% in bandwidth savings when using our protocol's compressed header.