Capacity management for streaming applications over cloud infrastructures with micro billing models

Tolosana-Calasanz, Rafael, Diaz-Montes, Javier, Bittencourt, Luiz F., Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646 and Parashar, Manish 2016. Capacity management for streaming applications over cloud infrastructures with micro billing models. Presented at: 9th International Conference on Utility and Cloud Computing, Shanghai, China, 6-9 December 2016. UCC '16 Proceedings of the 9th International Conference on Utility and Cloud Computing. ACM Press, p. 251. 10.1145/2996890.3007868

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Abstract

Recent advances in sensor technologies and instrumentation have led to an extraordinary growth of data sources and streaming applications. A wide variety of devices, from smart phones to dedicated sensors, have the capability of collecting and streaming data at unprecedented rates. Typical applications include smart cities & built environments for instance, where sensor-based infrastructures continue to increase in scale and variety. Analysis of stream data involves: (i) execution of a number of operations on a time/sample window - e.g. min./max./avg., filtering, etc; (ii) a need to combine a number of such operations together; (iii) event-driven execution of operations, generally over short time durations; (iv) operation correlations across multiple data streams. The use of such operations does not fit well in the per-hour or per-minute cloud billing models currently available from cloud providers - with some notable exceptions (e.g. Amazon AWS). In this paper we discuss how micro-billing and sub-second resource allocation can be used in the context of streaming applications and how micro-billing models bring challenges to capacity management on cloud infrastructures.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	ACM Press
ISBN:	9781450346160
Last Modified:	02 Nov 2022 10:02
URI:	https://orca.cardiff.ac.uk/id/eprint/97255

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