Pluto: High-Performance IoT-Aware Stream Processing

Abstract

Nowadays, large numbers of small IoT stream queries are created from diverse IoT applications and executed on cloud backend servers. However, existing distributed stream processing systems such as Storm and Flink do not efficiently handle the large numbers of IoT stream queries because of their tightly-coupled query/code submission layer and inefficient query execution layer. In this paper, we propose Pluto, a new IoT-aware stream processing system. As a first step for IoT stream processing, this paper focuses on optimizing the execution of many IoT stream queries on a node. Pluto optimizes the end-to-end query processing with a three-phase execution, harnessing IoT-query characteristics. First, Pluto minimizes bottlenecks in the IoT query submission by decoupling the code registration from the query submission process with new APIs, which eliminates duplicate code registration and enables code sharing across queries. Second, in the execution phase, Pluto shares system resources as much as possible and minimizes resource bottlenecks in a machine by exploiting commonalities among IoT stream queries and information exposed in the API. Our evaluations show that Pluto improves the throughput by an order of magnitude compared to other stream processing systems on a 24-core machine, keeping P99 latency less than one second.