High-throughput Real-time Edge Stream Processing with Topology-Aware Resource Matching

With the proliferation of Internet of Things (IoT) devices, real-time stream processing at the edge of the network has gained significant attention. However, edge stream processing systems face substantial challenges due to the heterogeneity and constraints of computational and network resources and the intricacies of multi-tenant application hosting. An optimized placement strategy for edge application topology becomes crucial to leverage the advantages offered by Edge computing and enhance the throughput and end-to-end latency of data streams. This paper presents Beaver, a resource scheduling framework designed to deploy stream processing topologies across distributed edge nodes efficiently. Its core is a novel scheduler that employs a synergistic integration of graph partitioning within application topologies and a two-sided matching technique to optimize the strategic placement of stream operators. Beaver aims to achieve optimal performance by minimizing bottlenecks in the network, memory, and CPU resources at the edge. We implemented a prototype of Beaver using Apache Storm and Kubernetes orchestration engine and evaluated its performance using an open-source real-time IoT benchmark (RIoTBench). Compared to state-of-the-art techniques, experimental evaluations demonstrate at least 1.6x improvement in the number of tuples processed within a one-second deadline under varying network delay and bandwidth scenarios.