Journal article
A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment
Abstract
Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the …
Authors
Zhu Y; Zhang W; Chen Y; Gao H
Journal
EURASIP Journal on Wireless Communications and Networking, Vol. 2019, No. 1,
Publisher
Springer Nature
Publication Date
December 2019
DOI
10.1186/s13638-019-1605-z
ISSN
1687-1472