| Autor: |
Andreazi GT; Computer System Division, Institute of Mathematics and Computer Science, University of São Paulo, São Paulo 13560-970, SP, Brazil., Estrella JC; Computer System Division, Institute of Mathematics and Computer Science, University of São Paulo, São Paulo 13560-970, SP, Brazil., Bruschi SM; Computer System Division, Institute of Mathematics and Computer Science, University of São Paulo, São Paulo 13560-970, SP, Brazil., Immich R; Computer Science Division, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil., Guidoni D; Computer Science Division, Federal University of São João del-Rei, São João del-Rei 36301-360, MG, Brazil., Alves Pereira Júnior L; Computer Science Division, Aeronautics Institute of Technology (ITA), São José dos Campos 12228-900, SP, Brazil., Meneguette RI; Computer System Division, Institute of Mathematics and Computer Science, University of São Paulo, São Paulo 13560-970, SP, Brazil. |
| Abstrakt: |
The high demand for data processing in web applications has grown in recent years due to the increased computing infrastructure supply as a service in a cloud computing ecosystem. This ecosystem offers benefits such as broad network access, elasticity, and resource sharing, among others. However, properly exploiting these benefits requires optimized provisioning of computational resources in the target infrastructure. Several studies in the literature improve the quality of this management, which involves enhancing the scalability of the infrastructure, either through cost management policies or strategies aimed at resource scaling. However, few studies adequately explore performance evaluation mechanisms. In this context, we present the MoHRiPA-Management of Hybrid Resources in Private cloud Architecture. MoHRiPA has a modular design encompassing scheduling algorithms, virtualization tools, and monitoring tools. The proposed architecture solution allows assessing the overall system's performance by using complete factorial planning to identify the general behavior of architecture under high demand of requests. It also evaluates workload behavior, the number of virtualized resources, and provides an elastic resource manager. A composite metric is also proposed and adopted as a criterion for resource scaling. This work presents a performance evaluation by using formal techniques, which analyses the scheduling algorithms of architecture and the experiment bottlenecks analysis, average response time, and latency. In summary, the proposed MoHRiPA mapping resources algorithm (HashRefresh) showed significant improvement results than the analyzed competitor, decreasing about 7% percent in the uniform average compared to ListSheduling (LS). |
