When FPGA-Accelerator Meets Stream Data Processing in the Edge

Abstract : Today, stream data applications represent the killer applications for Edge computing: placing computation close to the data source facilitates real-time analysis. Previous efforts have focused on introducing lightweight distributed stream processing (DSP) systems and dividing the computation between Edge servers and the clouds. Unfortunately, given the limited computation power of Edge servers, current efforts may fail in practice to achieve the desired latency of stream data applications. In this vision paper, we argue that by introducing FPGAs in Edge servers and integrating them into DSP systems, we might be able to realize stream data processing in Edge infrastructures. We demonstrate that through the design, implementation, and evaluation of F-Storm, an FPGA-accelerated and general-purpose distributed stream processing system on Edge servers. F-Storm integrates PCIe-based FPGAs into Edge-based stream processing systems and provides accelerators as a service for stream data applications. We evaluate F-Storm using different representative stream data applications. Our experiments show that compared to Storm, F-Storm reduces the latency by 36% and 75% for matrix multiplication and grep application. It also obtains 1.4x and 2.1x improvement for these two applications, respectively. We expect this work to accelerate progress in this domain.
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Submitted on : Tuesday, December 3, 2019 - 11:31:47 AM
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Song Wu, Die Hu, Shadi Ibrahim, Hai Jin, Jiang Xiao, et al.. When FPGA-Accelerator Meets Stream Data Processing in the Edge. ICDCS'19 - 39th IEEE International Conference on Distributed Computing Systems, Jul 2019, Dallas, United States. pp.1818-1829, ⟨10.1109/ICDCS.2019.00180⟩. ⟨hal-02389101⟩

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