SSPOC: Smart Stream Processing Operator Classification

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/304571
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Type: Examensarbete för masterexamen
Title: SSPOC: Smart Stream Processing Operator Classification
Authors: Nilsson, Hampus
Gustafsson, Victor
Abstract: Stream Processing is a rapidly growing field. Efficiently handling a stream processing query often requires knowing what type each operator is, as knowing its behaviour allows for tailored solutions. Today, each framework handles the identification of operators in its own way, often using semantics and compile-time info for this purpose. Having a more general way of classification could be an interesting way to simplify the creation of such framework. Creating such a general way requires a change from semantic info, as different frameworks use different semantics, to more general information. We pioneer a first step in this direction by using metrics available at runtime to classify a basic set of operators. In this thesis, we present a machine learning model for classification of stream processing operators. The model is a densely connected multi-layer feed-forward neural network. The operators that are classified are limited to a subset of the standard set of operators available in the stream processing framework Apache Flink. The training, validation and test datasets are also a contribution of this thesis. These were collected from public queries using our collection method. We also propose a set of features for our classifier, that aid in differentiating operators; we suggest that other machine-learning based solutions can use them.The model is optimized for prediction accuracy while training on data collected from 9 different queries. It reaches a prediction accuracy of 97.51% on the validation dataset and 99.796% on the test dataset.
Keywords: Computer;science;computer science;engineering;project;thesis;machine learning;neural networks;stream processing
Issue Date: 2019
Publisher: Chalmers tekniska högskola / Institutionen för data och informationsteknik
URI: https://hdl.handle.net/20.500.12380/304571
Collection:Examensarbeten för masterexamen // Master Theses



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