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OpenMSIStream provides seamless connection of scientific data stores with streaming infrastructure to allow researchers to leverage the power of decoupled, real-time data streaming architectures. Data streaming is the process of transmitting, ingesting, and processing data continuously rather than in batches. Access to streaming data has revolutionized many industries in the past decade and created entirely new standards of practice and types of analytics. While not yet commonly used in scientific research, data streaming has the potential to become a key technology to drive rapid advances in scientific data collection (e.g., Brookhaven National Lab (2022)). This paucity of streaming infrastructures linking complex scientific systems is due to a lack of tools that facilitate streaming in the diverse and distributed systems common in modern research. OpenMSIStream closes this gap between underlying streaming systems and common scientific infrastructure. Closing this gap empowers novel streaming applications for scientific data including automation of data curation, reduction, and analysis; real-time experiment monitoring and control; and flexible deployment of AI/ML to guide autonomous research. Streaming data generally refers to data continuously generated from multiple sources and passed in small packets (termed messages). Streaming data messages are typically organized in groups called topics and persist for periods of time conducive to processing for multiple uses either sequentially or in small groups. The resulting flows of raw data, metadata, and processing results form “ecosystems” that automate varied data-driven tasks. A strength of data streaming ecosystems is the use of publish-subscribe (“pub/sub”) messaging backbones that decouple data senders (publishers) and recipients (subscribers). Popular message-focused middleware solutions such as RabbitMQ (VMware, 2022), Apache Pulsar (Apache Software Foundation, 2022b), and Apache Kafka (Apache Software Foundation, 2022a) all provide differing capabilities as backbones. OpenMSIStream provides robust and efficient, yet easy, access to the rich data streaming systems of Apache Kafka.
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GOVERNOR: Smoother Stream Processing Through Smarter Backpressure
Big data systems have evolved beyond scalable storage and rudimentary processing to supporting complex data analytics in near real-time, such as Apache Spark Streaming [31], Comet [14], Incremental Hadoop [17], MapReduce Online [7], Apache Storm [28], StreamScope [19], and IBM Streams [1]. These systems are particularly challenging to build owing to two requirements: low latency and fault tolerance. Many of the above systems evolved from a batch processing design and are thus architected to break down a steady stream of input events into a series of micro-batches and then perform batch-like computations on each successive micro-batch as a micro-batch job. In terms of latency, the systems are expected to respond to each micro-batch in seconds with an output The constant operation further entails that the systems must be robust to hardware, software and network-level failures. To incorporate fault-tolerance, the common approach is to use checkpointing and rollback recovery, whereby a streaming application periodically saves its in-memory state to persistent storage.
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- Award ID(s):
- 1553579
- PAR ID:
- 10048564
- Date Published:
- Journal Name:
- 14th IEEE International Conference on Autonomous Computing
- Page Range / eLocation ID:
- 145 to 154
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICAC.2017.31
