Journal Article10.1016/J.FUTURE.2018.05.084
Measuring stream processing systems adaptability under dynamic workloads
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TL;DR: An index called AI-SPS inspired by the human cerebral auto-regulation process is proposed that quantifies the adaptation capacity of self-adaptive stream processing systems effectively and is validated by evaluating the adaptive behavior of two state of the art self- Adaptive streamprocessing systems.
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About: This article is published in Future Generation Computer Systems. The article was published on 01 Nov 2018. The article focuses on the topics: Data stream mining & Stream processing.
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References
Comparison of Static and Dynamic Cerebral Autoregulation Measurements
TL;DR: These data show that in normal human subjects measurement of dynamic autoregulation yields similar results as static testing of intact and pharmacologically impaired autoreGulation.
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Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming
Sanket Chintapalli,Derek Dagit,Bobby Evans,Reza Farivar,Thomas Graves,Mark Holderbaugh,Zhuo Liu,Kyle Nusbaum,Kishorkumar Patil,Boyang Jerry Peng,Paul Poulosky +10 more
- 23 May 2016
TL;DR: A streaming benchmark for three representative computation engines: Flink, Storm and Spark Streaming is developed and a performance comparison of the three data engines in terms of 99th percentile latency and throughput for various configurations is provided.
358
Elastic Scaling for Data Stream Processing
TL;DR: This article proposes an elastic auto-parallelization solution that can dynamically adjust the number of channels used to achieve high throughput without unnecessarily wasting resources and can handle partitioned stateful operators via run-time state migration, which is fully transparent to the application developers.
Esc: Towards an Elastic Stream Computing Platform for the Cloud
Benjamin Satzger,Waldemar Hummer,Philipp Leitner,Schahram Dustdar +3 more
- 04 Jul 2011
TL;DR: ES is a new stream computing engine designed for computations with real-time demands, such as online data mining, that offers a simple programming model in which programs are specified by directed acyclic graphs (DAGs).