Host load prediction with long short-term memory in cloud computing

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-t...

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Bibliographic Details
Published in:The Journal of supercomputing 2018-12, Vol.74 (12), p.6554-6568
Main Authors: Song, Binbin, Yu, Yao, Zhou, Yu, Wang, Ziqiang, Du, Sidan
Format: Article
Language:English
Subjects:
Adaptive systems
Cloud computing
Compilers
Computer memory
Computer networks
Computer Science
Interpreters
Performance evaluation
Processor Architectures
Programming Languages
Resource allocation
Short term
State of the art
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-017-2044-4