dc.creator.ID |
ALMEIDA, M. M. S. C. |
pt_BR |
dc.creator.Lattes |
http://lattes.cnpq.br/6098150643100265 |
pt_BR |
dc.contributor.advisor1 |
SILVA, Thiago Emmanuel Pereira da Cunha. |
|
dc.contributor.advisor1ID |
SILVA, T. E. P. C. |
pt_BR |
dc.contributor.referee1 |
NICOLLETTI, Pedro Sergio. |
|
dc.contributor.referee1ID |
NICOLLETTI, P. S. |
pt_BR |
dc.contributor.referee2 |
BRASILEIRO, Francisco Vilar. |
|
dc.contributor.referee2ID |
BRASILEIRO, F. V. |
pt_BR |
dc.contributor.referee2Lattes |
http://lattes.cnpq.br/5957855817378897 |
pt_BR |
dc.publisher.country |
Brasil |
pt_BR |
dc.publisher.department |
Centro de Engenharia Elétrica e Informática - CEEI |
pt_BR |
dc.publisher.initials |
UFCG |
pt_BR |
dc.subject.cnpq |
Ciência da Computação. |
pt_BR |
dc.title |
A case study of proactive auto-scaling for an ecommerce workload. |
pt_BR |
dc.date.issued |
2022-09-02 |
|
dc.description.abstract |
Preliminary data obtained from a partnership between the Federal University of Campina Grande
and an ecommerce company indicates that some applications have issues when dealing with variable
demand. This happens because a delay in scaling resources leads to performance degradation and, in
literature, is a matter usually treated by improving the auto-scaling. To better understand the current
state-of-the-art on this subject, we re-evaluate an auto-scaling algorithm proposed in the literature,
in the context of ecommerce, using a long-term real workload. Experimental results show that our
proactive approach is able to achieve an accuracy of up to 94 percent and led the auto-scaling to a
better performance than the reactive approach currently used by the ecommerce company. |
pt_BR |
dc.identifier.uri |
http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/29266 |
|
dc.date.accessioned |
2023-04-05T16:43:39Z |
|
dc.date.available |
2023-04-05 |
|
dc.date.available |
2023-04-05T16:43:39Z |
|
dc.type |
Trabalho de Conclusão de Curso |
pt_BR |
dc.subject |
Ecommerce workload |
pt_BR |
dc.subject |
Case study |
pt_BR |
dc.subject |
Estudos de caso |
pt_BR |
dc.subject |
Cloud computing |
pt_BR |
dc.subject |
Auto-scaling |
pt_BR |
dc.subject |
ARIMA |
pt_BR |
dc.subject |
Workload prediction |
pt_BR |
dc.subject |
Algoritmo de autoescalonamento |
pt_BR |
dc.rights |
Acesso Aberto |
pt_BR |
dc.creator |
ALMEIDA, Marcella Medeiros Siqueira Coutinho de. |
|
dc.publisher |
Universidade Federal de Campina Grande |
pt_BR |
dc.language |
eng |
pt_BR |
dc.title.alternative |
Um estudo de caso de dimensionamento automático proativo para uma carga de trabalho de comércio eletrônico. |
pt_BR |
dc.identifier.citation |
ALMEIDA, Marcella Medeiros Siqueira Coutinho de. A case study of proactive auto-scaling for an ecommerce workload. 2022. 10f. (Trabalho de Conclusão de Curso - Artigo), Curso de Bacharelado em Ciência da Computação, Centro de Engenharia Elétrica e Informática , Universidade Federal de Campina Grande – Paraíba - Brasil, 2022. Disponível em: http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/29266 |
pt_BR |