Assessing cloud QoS predictions using OWA in neural network methods
Journal article
Hussain, Walayat, Gao, Honghao, Raza, Muhammad Raheel, Rabhi, Fethi A. and Merigó, Jose M.. (2022). Assessing cloud QoS predictions using OWA in neural network methods. Neural Computing and Applications. 34(17), pp. 14895-14912. https://doi.org/10.1007/s00521-022-07297-z
Authors | Hussain, Walayat, Gao, Honghao, Raza, Muhammad Raheel, Rabhi, Fethi A. and Merigó, Jose M. |
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Abstract | Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy. |
Keywords | computational complexity; time-series forecasting; cloud QoS; deep neural network; complex prediction; OWA; service level agreement |
Year | 2022 |
Journal | Neural Computing and Applications |
Journal citation | 34 (17), pp. 14895-14912 |
Publisher | Springer |
ISSN | 0941-0643 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00521-022-07297-z |
PubMed ID | 35599973 |
Scopus EID | 2-s2.0-85130179559 |
PubMed Central ID | PMC9107219 |
Open access | Published as ‘gold’ (paid) open access |
Page range | 14895-14912 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 14 May 2022 |
Publication process dates | |
Accepted | 13 Apr 2022 |
Deposited | 17 Jul 2023 |
https://acuresearchbank.acu.edu.au/item/8z4z1/assessing-cloud-qos-predictions-using-owa-in-neural-network-methods
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Publisher's version
OA_Hussain_2022_Assessing_cloud_QoS_predictions_using_OWA.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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