Accommodating bursts in distributed stream processing systems
Distributed Stream Processing frameworks are being commonly used with the evolution of Internet of Things(Io T).
In this article, we propose a model-driven approach for scheduling streaming applications that effectively utilizes a priori knowledge of the applications to provide predictable scheduling behavior.
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Stream processing systems have become important, as applications like media broadcasting, sensor network monitoring and on-line data analysis increasingly rely on realtime stream processing.
Stream processing systems have become important, as applications like media broadcasting, sensor network monitoring and on-line data analysis increasingly rely on real-time stream processing.
Our experimental results over our Synergy distributed stream processing system demonstrate the efficiency of our approach.
The BOM stated that “in accordance with records management practices”, the field books for early 2013 at Canberra Airport were “disposed of” twelve months after the observations were taken.