A Data Driven Priority Scheduling technique for a Stream Processing Platform

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  • Big data processing has become essential for businesses in recent years as it enables organizations to gather insights from streaming data in near real-time and capitalize on business opportunities. One drawback of stream processing engines is the lack of support for priority scheduling. There are cases where businesses need to ensure that important input data items are processed with low latencies thus avoiding a missed business opportunity. This thesis proposes a technique that enables users to prioritize important input data so that they are processed in time even when the system is under high or bursty input load. Using a prototype this thesis demonstrates the efficacy of the proposed technique. Performance analysis demonstrates that there is a significant latency improvement for high priority data over low priority data especially when there is high system contention.

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  • Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2019


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