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Sensors 2016, 16(4), 588;

Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics

Department of Software Engineering, China University of Petroleum, No. 66 Changjiang West Road, Qingdao 266031, China
Hisense TransTech Co., Ltd., No. 16 Shandong Road, Qingdao 266031, China
Authors to whom correspondence should be addressed.
Academic Editors: Yunchuan Sun, Antonio Jara and Shengling Wang
Received: 19 January 2016 / Revised: 26 March 2016 / Accepted: 18 April 2016 / Published: 23 April 2016
(This article belongs to the Special Issue Identification, Information & Knowledge in the Internet of Things)
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Storm-based stream processing is widely used for real-time large-scale distributed processing. Knowing the run-time status and ensuring performance is critical to providing expected dependability for some applications, e.g., continuous video processing for security surveillance. The existing scheduling strategies’ granularity is too coarse to have good performance, and mainly considers network resources without computing resources while scheduling. In this paper, we propose Healthcare4Storm, a framework that finds Storm insights based on Storm metrics to gain knowledge from the health status of an application, finally ending up with smart scheduling decisions. It takes into account both network and computing resources and conducts scheduling at a fine-grained level using tuples instead of topologies. The comprehensive evaluation shows that the proposed framework has good performance and can improve the dependability of the Storm-based applications. View Full-Text
Keywords: storm metrics; CPU-GPU; scheduling; optimization storm metrics; CPU-GPU; scheduling; optimization

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zhang, W.; Duan, P.; Chen, X.; Lu, Q. Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics. Sensors 2016, 16, 588.

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