Name
Abstract | ||
|---|---|---|
Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently. This paper proposes a LSTM (long and short-term memory model) based service migration approach for pervasive cloud computing, i.e., LSTM4PCC, which supports an accurate prediction of cloud resources. LSTM4PCC makes a prediction for cloud resource availability with a LSTM network and establishes a service migration mechanism in order to optimize service executions. We evaluate LSTM4PCC and compare it with the ARIMA (AutoRegressive Integrated Moving Average) approach in terms of prediction accuracy. The results show that LSTM4PCC performs better than ARIMA. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/Cybermatics_2018.2018.00305 | 2018 IEEE 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 computing,Task analysis,Predictive models,Monitoring,Computer architecture,Mobile handsets,Time series analysis | Cloud resources,Time series,Task analysis,Computer science,Autoregressive integrated moving average,Memory model,Distributed computing,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-7975-3 | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haifeng Jing | 1 | 1 | 0.35 |
| Yafei Zhang | 2 | 100 | 13.82 |
| Jiehan Zhou | 3 | 226 | 28.61 |
| Weishan Zhang | 4 | 396 | 52.57 |
| Xin Liu | 5 | 82 | 12.27 |
| Guizhi Min | 6 | 1 | 1.03 |
| Zhanmin Zhang | 7 | 1 | 0.69 |