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Jeon, Myeongjae
Research Interests
  • Parallel/distributed processing of deep learning workloads, Real-time stream data analytics at cloud/IoT scale, Public/private blockchain

StreamBox: Modern Stream Processing on a Multicore Machine

DC Field Value Language Miao, Hongyu ko Park, Heejin ko Jeon, Myeongjae ko Pekhimenko, Gennady ko McKinley, Kathryn S. ko Lin, Felix Xiaozhu ko 2020-07-24T06:10:17Z - 2018-08-27 ko 2017-07-12 ko
dc.identifier.citation USENIX Annual Technocal Conference ko
dc.identifier.uri -
dc.description.abstract Stream analytics on real-time events has an insatiable demand for throughput and latency. Its performance on a single machine is central to meeting this demand, even in a distributed system. This paper presents a novel stream processing engine called StreamBox that exploits the parallelism and memory hierarchy of modern multicore hardware. StreamBox executes a pipeline of transforms over records that may arrive out-of-order. As records arrive, it groups the records into ordered epochs delineated by watermarks. A watermark guarantees no subsequent record’s event timestamp will precede it. Our contribution is to produce and manage abundant parallelism by generalizing out-of-order record processing within each epoch to out-of-order epoch processing and by dynamically prioritizing epochs to optimize latency. We introduce a data structure called cascading containers, which dynamically manages concurrency and dependences among epochs in the transform pipeline. StreamBox creates sequential memory layout of records in epochs and steers them to optimize NUMA locality. On a 56-core machine, StreamBox processes records up to 38 GB/sec (38M Records/sec) with 50 ms latency. ko
dc.language 영어 ko
dc.publisher USENIX ko
dc.title StreamBox: Modern Stream Processing on a Multicore Machine ko
dc.type CONFERENCE ko
dc.identifier.scopusid 2-s2.0-85077449843 ko
dc.type.rims CONF ko
dc.identifier.url ko
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