Name
Abstract | ||
|---|---|---|
Host load prediction is significant for improving resource allocation and utilization in cloud computing. Due to the higher variance than that in a grid, accurate prediction remains a challenge in the cloud system. In this paper, we apply a concise yet adaptive and powerful model called long short-term memory to predict the mean load over consecutive future time intervals and actual load multi-step-ahead. Two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, and the other is that in a traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11227-017-2044-4 | The Journal of Supercomputing |
Keywords | Field | DocType |
Host load prediction, Cloud computing, Long short-term memory, Multi-step-ahead | Computer science,Long short term memory,Real-time computing,Resource allocation,Data center,Grid,Cloud computing,Distributed computing | Journal |
Volume | Issue | ISSN |
74 | 12 | 0920-8542 |
Citations | PageRank | References |
23 | 0.75 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binbin Song | 1 | 23 | 0.75 |
| Yao Yu | 2 | 104 | 11.90 |
| Yu Zhou | 3 | 56 | 6.07 |
| Ziqiang Wang | 4 | 23 | 1.08 |
| Sidan Du | 5 | 314 | 31.20 |