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Showing 1 to 8 of 8 for “"Stream processing systems"”.

  1. Satisfying service level objectives in stream processing systems

    … sites that show top trends and recent comments, streaming video analytics that identify traffic patterns and movement, and jobs that process ad pipelines. This has led to the proliferation of stream processing systems that process such data to produce real-time results. As these applications must …

    uiuc Repository record for Satisfying service level objectives in stream processing systems (opens in a new tab)

  2. Segment backup: datacenter-disaster tolerance for stream processing systems

    … tolerance solution called segment backup for stream processing systems. During regular running, segment backup inserts barriers into the normal tuple stream to indicate the backup versions and ensure node-level synchronization of multiple input streams. Additionally, segment backup …

    uiuc Repository record for Segment backup: datacenter-disaster tolerance for stream processing systems (opens in a new tab)

  3. Stela: on-demand elasticity in distributed data stream processing systems

    … velocity [24], and recently several real- time stream processing systems have emerged to combat this challenge. These systems process streams of data in real time and computational results. However, current popular data stream processing systems lack the ability to scale out and scale in (i.e., …

    uiuc Repository record for Stela: on-demand elasticity in distributed data stream processing systems (opens in a new tab)

  4. New techniques to lower the tail latency in stream processing systems

    Over the past decade, the demand for real time processing of huge amount of streaming data has emerged and grown rapidly. Apache Storm, Apache Flink, Samza and many other stream processing frameworks have been proposed and implemented to meet this need. Although lots of effort has been made to …

    uiuc Repository record for New techniques to lower the tail latency in stream processing systems (opens in a new tab)

  5. Elasticity and resource aware scheduling in distributed data stream processing systems

    The student, Boyang Peng, submitted this Thesis for approval on 2015-04-22 at 10:43.

    uiuc Repository record for Elasticity and resource aware scheduling in distributed data stream processing systems (opens in a new tab)

  6. Dynamic re-optimization techniques for stream processing engines and object stores

    <p>Large scale data storage and processing systems are strongly motivated by the need to store and analyze massive datasets. The complexity of a large class of these systems is rooted in their distributed nature, extreme scale, need for real-time response, and streaming nature. The use of these …

    purdue-thes Repository record for Dynamic re-optimization techniques for stream processing engines and object stores (opens in a new tab)

  7. Efficient parallel processing and fault tolerance in a streaming join system

    Stream joins are an important component of stream processing, as they provide an online mechanism to efficiently combine multiple streams of data. In this thesis, we consider the RiverJoin system, which presents a general method for performing stream joins without relying on the ordering and timing …

    mit Repository record for Efficient parallel processing and fault tolerance in a streaming join system (opens in a new tab)

  8. Design automation and QoS requirements preservation for multiprocessor embedded systems

    … number of processors is increasing in embedded systems but the usefulness of parallel computation is not better leveraged due to the inflexibility of design and implementation for multiprocessor embedded system applications. A higher level abstraction (i.e., a parallel programming framework) can …

    uoit Repository record for Design automation and QoS requirements preservation for multiprocessor embedded systems (opens in a new tab)