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DIsCO: DynamIc Data COmpression in Distributed Stream Processing Systems

  • Nikos Zacheilas
  • Vana Kalogeraki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10320)

Abstract

Supporting high throughput in Distributed Stream Processing Systems (DSPSs) has been an important goal in recent years. Current works either focus on automatically increasing the system resources whenever the current setup is inadequate or apply load shedding techniques discarding some of the incoming data. However, both approaches have significant shortcomings as they require on the fly application reconfiguration where the application needs to be stopped and re-uploaded in the cluster with the new configurations, and can lead to significant information loss. One approach that has not yet been considered for improving the throughput of DSPSs is exploiting compression algorithms to minimize the communication overhead between components especially in cases where we have large-sized data like live CCTV camera reports. This work is the first that provides a novel framework, built on top of Apache Storm, which enables dynamic compression of incoming streaming data. Our approach uses a profiling algorithm to automatically determine the compression algorithm that should be applied and supports both lossless and lossy compression techniques. Furthermore, we propose a novel algorithm for determining when profiling should be applied. Finally, our detailed experimental evaluation with commonly used stream processing applications, indicates a clear improvement on the applications’ throughput when our proposed techniques are applied.

Keywords

Utility Score Compression Algorithm Compression Technique JPEG Compression Special Thread 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This research has been financed by the European Union through the FP7 ERC IDEAS 308019 NGHCS project and the Horizon2020 688380 VaVeL project.

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Athens University of Economics and BusinessAthensGreece

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