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Title: A Deep Recurrent Neural Network Based Predictive Control Framework for Reliable Distributed Stream Data Processing
In this paper, we present design, implementation and evaluation of a novel predictive control framework to enable reliable distributed stream data processing, which features a Deep Recurrent Neural Network (DRNN) model for performance prediction, and dynamic grouping for flexible control. Specifically, we present a novel DRNN model, which makes accurate performance prediction with careful consideration for interference of co-located worker processes, according to multilevel runtime statistics. Moreover, we design a new grouping method, dynamic grouping, which can distribute/re-distribute data tuples to downstream tasks according to any given split ratio on the fly. So it can be used to re-direct data tuples to bypass misbehaving workers. We implemented the proposed framework based on a widely used Distributed Stream Data Processing System (DSDPS), Storm. For validation and performance evaluation, we developed two representative stream data processing applications: Windowed URL Count and Continuous Queries. Extensive experimental results show: 1) The proposed DRNN model outperforms widely used baseline solutions, ARIMA and SVR, in terms of prediction accuracy; 2) dynamic grouping works as expected; and 3) the proposed framework enhances reliability by offering minor performance degradation with misbehaving workers.  more » « less
Award ID(s):
1704662
NSF-PAR ID:
10177395
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the IEEE Symposium on Parallel and Distributed Processing
ISSN:
1063-6374
Page Range / eLocation ID:
262-272
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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