dc.contributor.author | Chen, Z | |
dc.contributor.author | Hu, J | |
dc.contributor.author | Min, G | |
dc.contributor.author | Zomaya, AY | |
dc.contributor.author | El-Ghazawi, T | |
dc.date.accessioned | 2019-11-20T15:11:21Z | |
dc.date.issued | 2019-11-15 | |
dc.description.abstract | Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However,
the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting
and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data
analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning longterm memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we
propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed
to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA
and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using realworld workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to
demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover,
the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other
workload prediction methods for high-dimensional and highly-variable real-world cloud workloads. | en_GB |
dc.identifier.citation | Published online 15 November 2019 | en_GB |
dc.identifier.doi | 10.1109/TPDS.2019.2953745 | |
dc.identifier.uri | http://hdl.handle.net/10871/39622 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including
reprinting/republishing this material for advertising or promotional purposes, creating new
collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted
component of this work in other works. | en_GB |
dc.subject | Cloud computing | en_GB |
dc.subject | workload prediction | en_GB |
dc.subject | resource provisioning | en_GB |
dc.subject | sequential data analysis | en_GB |
dc.subject | deep learning | en_GB |
dc.title | Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-11-20T15:11:21Z | |
dc.identifier.issn | 1045-9219 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.identifier.journal | IEEE Transactions on Parallel and Distributed Systems | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2019-11-09 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2019-11-09 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-11-12T22:49:53Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-11-29T12:02:23Z | |
refterms.panel | B | en_GB |