Accommodating bursts in distributed stream processing systems updating data in pivot table

27-May-2020 23:57

To minimize the effects of input rate bursts upon the system, we take advantage of a load shedding method while keeping the Qo S guarantees made by the system based on the SO scheme and the CBP model.Finally, we substantiate the utility of our work through experiments on ns-3.There has been an explosion of innovation in open source stream processing over the past few years.Frameworks such as Apache Spark and Apache Storm give developers stream abstractions on which they can develop applications; Apache Beam provides an API abstraction, enabling developers to write code independent of the underlying framework, while tools such as Apache Ni Fi and Stream Sets Data Collector provide a user interface abstraction, allowing data engineers to define data flows from high-level building blocks with little or no coding.

accommodating bursts in distributed stream processing systems-17

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Examples include road traffic networks, processing of financial feeds, network monitoring and real-time sensor data analysis systems.

Topics covered by the other papers include: computer-related costs, evaluating computer center personnel, managing software development using the critical path method, selecting microcomputer network configurations, networking library and computer services, electronic mail, decision support systems for strategic planning, a statewide reporting system, and the use of new technologies at specific colleges.

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Our approach builds application data rate patterns at run-time and predicts the effect of the burst on the performance of the applications, to identify whether there is a need to react on the onset of a burst.

Our detailed experimental results over our Synergy middleware illustrate that our approach is practical, depicts good performance and has low resource overhead. We present an economical and fault-tolerant load balancing strategy (EFTLBS) based on an operator replication mechanism and a load shedding method, that fully utilizes the network resources to realize continuous and highly-available data stream processing without dynamic operator migration over wide area networks.

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