Multi-Layer Neural Networks for Quality of Service oriented Server-State Classification in Cloud Servers
File(s)
Accepted version
Author(s)
Yin, Yonghua
Wang, Lan
Gelenbe, Erol
Type
Conference Paper
Abstract
Task allocation systems in the Cloud have been recently proposed so that their performance is optimised in real-time based on reinforcement learning with spiking Random Neural Networks (RNN). In this paper, rather than reinforcement learning, we suggest the use of multi-layer neural network architectures to infer the state of servers in a dynamic networked Cloud environment, and propose to select the most adequate server based on the task that optimises Quality of Service. First, a procedure is presented to construct datasets for state classification by collecting time-varying data from Cloud servers that have different resource configurations, so that the identification of server states is carried out with supervised classification. We test four distinct multi-layer neural network architectures to this effect: multi-layer dense clusters of RNNs (MLRNN), the hierarchical extreme learning machine (H-ELM), the multi-layer perceptron, and convolutional neural networks. Our experimental results indicate that server-state identification can be carried out efficiently and with the best accuracy using the MLRNN and H-ELM.
Date Issued
2017-07-03
Date Acceptance
2017-05-14
Citation
2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, pp.1623-1627
ISSN
2161-4393
Publisher
IEEE
Start Page
1623
End Page
1627
Journal / Book Title
2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Copyright Statement
©2017 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.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
European Commission Directorate-General for Research and Innovation
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000426968701119&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
N/A
EU H2020 Framework Prog. R & Innovation Grant Agreement 727528
Source
International Joint Conference on Neural Networks (IJCNN)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Engineering
BIG DATA
DEEP
Publication Status
Published
Start Date
2017-05-14
Finish Date
2017-05-19
Coverage Spatial
Anchorage, AK
Date Publish Online
2017-07-03