Graphite Monitoring and Graphs

Sharma, Vishal

doi:10.1007/978-1-4842-1694-1_6

Cited by 2 publications

(2 citation statements)

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“…We retrieved the timestamps of the last 5 'dweets' from the sample of IoT ings in Section 3.1 over a 24 hour period and observed that, of those available, 62.1% are unevenly-spaced while [34] or the Whisper database which was designed as a storage backend for the Graphite stack [51] are less suitable for handling the IoT's variable spacing time-series data. Both even and unevenly-spaced data were also present across the studies shown in Table 2.…”

Section: Evenly-spaced Vs Unevenly-spaced Iot Datamentioning

confidence: 99%

TritanDB: Time-series Rapid Internet of Things Analytics

Siow¹,

Tiropanis²,

Wang³

et al. 2018

Preprint

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e e cient management of data is an important prerequisite for realising the potential of the Internet of ings (IoT). Two issues given the large volume of structured time-series IoT data are, addressing the di culties of data integration between heterogeneous ings and improving ingestion and query performance across databases on both resource-constrained ings and in the cloud. In this paper, we examine the structure of public IoT data and discover that the majority exhibit unique at, wide and numerical characteristics with a mix of evenly and unevenly-spaced time-series. We investigate the advances in time-series databases for telemetry data and combine these ndings with microbenchmarks to determine the best compression techniques and storage data structures to inform the design of a novel solution optimised for IoT data. A query translation method with low overhead even on resource-constrained ings allows us to utilise rich data models like the Resource Description Framework (RDF) for interoperability and data integration on top of the optimised storage. Our solution, TritanDB, shows an order of magnitude performance improvement across both ings and cloud hardware on many state-of-the-art databases within IoT scenarios. Finally, we describe how TritanDB supports various analyses of IoT time-series data like forecasting.

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Section: Evenly-spaced Vs Unevenly-spaced Iot Datamentioning

confidence: 99%

TritanDB: Time-series Rapid Internet of Things Analytics

Siow¹,

Tiropanis²,

Wang³

et al. 2018

Preprint

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show abstract

“…The WSO 2 IoT server provides the various built-in dashboard plugins which are easy to configure and deploy. However, for larger systems, some of the enterprise scale real-time dashboards such as ElasticCompute Logstash Kibana (ELK) stack [81], Ganglia [82], Graphite [83], and Datadog [84] can also be configured with the WSO 2 IoT server.…”

Section: Real-time Dashboardsmentioning

confidence: 99%

An Edge Computing-Based Complex Event Processing Technique for Sensor-Based Systems

Dhillon¹

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Complex Event Processing (CEP) on sensor-based systems often uses a mobile gateway agent to forward raw sensor data streams to a remote back-end server. Complex events that are triggered by multiple raw events are then detected at the back-end server. This approach relies on a persistent network connection between the back-end server and the mobile device. This thesis proposes an edge computing-based mobile CEP technique in which CEP is performed on the mobile edge device using an embedded CEP engine and the detected complex events are sent to the back-end server for further processing. A proof-of-concept prototype for this system has been built using a Siddhi CEP engine and a WSO 2 server. A thorough performance analysis is performed for comparing the proposed system with the back-end server-based system. The proposed system can handle intermittent network disconnections and leads to reduced user cost and energy consumption for the mobile device.

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Graphite Monitoring and Graphs

Cited by 2 publications

References 0 publications

TritanDB: Time-series Rapid Internet of Things Analytics

TritanDB: Time-series Rapid Internet of Things Analytics

An Edge Computing-Based Complex Event Processing Technique for Sensor-Based Systems

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