Workload Prediction for IoT Data Management Systems
| dc.contributor.author | Burrell, David | |
| dc.contributor.author | Chatziliadis, Xenofon | |
| dc.contributor.author | Zacharatou, Eleni Tzirita | |
| dc.contributor.author | Zeuch, Steffen | |
| dc.contributor.author | Markl, Volker | |
| dc.contributor.editor | König-Ries, Birgitta | |
| dc.contributor.editor | Scherzinger, Stefanie | |
| dc.contributor.editor | Lehner, Wolfgang | |
| dc.contributor.editor | Vossen, Gottfried | |
| dc.date.accessioned | 2023-02-23T14:00:14Z | |
| dc.date.available | 2023-02-23T14:00:14Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The Internet of Things (IoT) is an emerging technology that allows numerous devices, potentially spread over a large geographical area, to collect and collectively process data from high-speed data streams.To that end, specialized IoT data management systems (IoTDMSs) have emerged.One challenge in those systems is the collection of different metrics from devices in a central location for analysis. This analysis allows IoTDMSs to maintain an overview of the workload on different devices and to optimize their processing. However, as an IoT network comprises of many heterogeneous devices with low computation resources and limited bandwidth, collecting and sending workload metrics can cause increased latency in data processing tasks across the network.In this ongoing work, we present an approach to avoid unnecessary transmission of workload metrics by predicting CPU, memory, and network usage using machine learning (ML).Specifically, we demonstrate the performance of two ML models, linear regression and Long Short-Term Memory (LSTM) neural network, and show the features that we explored to train these models.This work is part of an ongoing research to develop a monitoring tool for our new IoTDMS named NebulaStream. | en |
| dc.identifier.doi | 10.18420/BTW2023-64 | |
| dc.identifier.isbn | 978-3-88579-725-8 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40373 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BTW 2023 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-331 | |
| dc.subject | Internet of Things | |
| dc.subject | stream processing | |
| dc.subject | machine learning | |
| dc.subject | workload prediction | |
| dc.title | Workload Prediction for IoT Data Management Systems | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 950 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 943 | |
| gi.conference.date | 06.-10. März 2023 | |
| gi.conference.location | Dresden, Germany |
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