SCALING DATA STREAM PROCESSING ON MULTICORE ARCHITECTURES

Abstract

Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. Witnessing the emergence of modern commodity machines with massively parallel processors, researchers and practitioners find shared-memory multicore architectures an attractive platform for DSPSs. However, fully exploiting the computation power delivered by multicore architectures can be challenging, and scaling DSPSs on modern multicore architectures remains to be notoriously challenging. This is because processing massive amounts of data can confront several performance bottlenecks inherited from different DSPS components, which altogether put a strict constraint on the scalability of the DSPSs on multicore architectures. In this thesis, we present the evaluation, design and implementation of DSPSs aiming at high-performance stream processing on multicore architectures.