Detalhes bibliográficos
Ano de defesa: |
2017 |
Autor(a) principal: |
SILVA, Francisco Airton Pereira da |
Orientador(a): |
MACIEL, Paulo Romero Martins |
Banca de defesa: |
Não Informado pela instituição |
Tipo de documento: |
Tese
|
Tipo de acesso: |
Acesso aberto |
Idioma: |
eng |
Instituição de defesa: |
Universidade Federal de Pernambuco
|
Programa de Pós-Graduação: |
Programa de Pos Graduacao em Ciencia da Computacao
|
Departamento: |
Não Informado pela instituição
|
País: |
Brasil
|
Palavras-chave em Português: |
|
Link de acesso: |
https://repositorio.ufpe.br/handle/123456789/25228
|
Resumo: |
Resource scarcity is a major obstacle for many mobile applications, since devices have limited battery and processing power. The use of cloud computing has been shown to be a feasible alternative to process demanding mobile devices workloads, leading to the research field called mobile cloud computing (MCC). By using the cloud, mobile devices may offload computation to resourceful servers. Many issues related to such a process have been investigated in the past decade, but those related to offloading process still remain. This PhD research has developed a smart MCC offloading strategy for mobile applications. The approach have considered an innovative balanced infrastructure parameters strategy. Another MCC challenge is related to the process of infrastructure evaluation and planning. Evaluating the MCC infrastructure in a deep level of detail may provide to software engineers precise information, guiding their decisions. Instead of evaluating the MCC infrastructure as a black-box, this work proposes to analyze the application at source-code level. This PhD research proposes providing a way for representing method-calls and evaluating mobile cloud applications by using stochastic petri nets (SPNs). The SPNs in this work allow software engineers to understand their applications through a statistic report. Case studies have showed that the proposed techniques are helpful for guiding cloud systems designers and administrators in the decision-making process. |