Autor(es)

Tolosana-Calasanz, Rafael ; Diaz-Montes, Javier ; Bittencourt, Luiz F. ; Rana, Omer ; Parashar, Manish ; IEEE

• 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