Name
Abstract | ||
|---|---|---|
The development of cloud computing has promoted the development of predictive technology. A number of prediction methods have been proposed, but these methods require a lot of manual operations to find the appropriate parameters. This caused great inconvenience. Therefore, this paper designs and implements a tool that allows users to select models and independently test the optimal combination of parameters. RPT is an openly improvable toolbox that permits users to directly establish an elastic offline resource forecast system in which resources are furnished by the user. The system consists of a number of scalable prediction modules. These are dataset import, model selection, parameter adjustment, and prediction results. The system can be widely used to predict the use of cloud resources in the future, and to select appropriate evaluation indicators to evaluate the performance of the model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00096 | 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) |
Keywords | Field | DocType |
cloud resources, prediction model, optimal parameter combination | Cloud resources,Data modeling,Load modeling,Computer science,Toolbox,Model selection,Extensibility,Distributed computing,Cloud computing,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-7281-2981-5 | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Wang | 1 | 0 | 0.34 |
| Yiping Wen | 2 | 25 | 8.59 |
| Yu Zhang | 3 | 0 | 0.34 |
| Jinjun Chen | 4 | 0 | 0.34 |