| Texto completo | |
| Autor(es): |
Tolosana-Calasanz, Rafael
;
Diaz-Montes, Javier
;
Bittencourt, Luiz F.
;
Rana, Omer
;
Parashar, Manish
;
IEEE
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2016 IEEE/ACM 9TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC); v. N/A, p. 6-pg., 2016-01-01. |
| Resumo | |
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 microbilling 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. (AU) | |
| Processo FAPESP: | 15/16332-8 - Escalonamento em nuvens autonômicas |
| Beneficiário: | Luiz Fernando Bittencourt |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |