Multi-Step-Ahead Web Service QoS Time Series Forecasting: A Multi-Predictor-Based Genetic Programming Approach
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- @InProceedings{syu:2023:GECCOcomp,
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author = "Yang Syu and Yong-Yi Fanjiang",
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title = "Multi-Step-Ahead Web Service {QoS} Time Series
Forecasting: A Multi-Predictor-Based Genetic
Programming Approach",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Alberto Moraglio",
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pages = "43--44",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, web services,
machine learning, time series forecasting,
service-oriented software engineering",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3595841",
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size = "2 pages",
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abstract = "Previously, in a GECCO 2022 Hot-off-the-Press paper
[1], we presented a comprehensive survey of the
modeling and prediction of Web service (WS) quality of
service (QoS) time series [2]. Based on the exhaustive
investigation in [2], this research subject has already
been deeply and widely studied for over a decade; for
the one-step-ahead version of this problem, which can
be considered its most primitive problem form, overall,
our proposed and developed genetic programming
(GP)-based solution outperforms competitors in terms of
both modeling and forecasting accuracy, according to
our ongoing study, which has been reported in [3] [4]
[5]. Nevertheless, as argued in [6], for the long-term
use and rental of cloud-based WSs, multi-step-ahead QoS
time series prediction of these services is needed.
Thus, the authors employed and revised the two most
widely used single-predictor-based time series methods,
namely, autoregressive integrated moving average
(ARIMA) models and exponential smoothing (ES), to
address this latest version of the problem.For this
multi-step-ahead variant of the problem, in Y. Y.
Fanjiang, Y. Syu and W. L. Huang, {"}Time Series QoS
Forecasting for Web Services Using
Multi-Predictor-based Genetic Programming{"}, IEEE
Transactions on Services Computing (TSC), Vol. 15, P.P.
1423--1435, 2022, we devise and employ a
multipredictor-based approach to genetic programming.
First, due to its superiority in our past work for the
basic (i.e., one-step-ahead) version of the problem, we
investigate the performance of GP on this newly emerged
(multi-step-ahead) form of the problem. Second, instead
of using a single model for predictions regarding
multiple future time points, which is the method
commonly adopted in prior research [2], we evolve and
apply a dedicated GP-generated predictor for each
targeted future time point and its projection.
Furthermore, two different strategies for the consumed
predictor inputs are tested to determine their
differences and influence on accuracy so that a better
strategy can be empirically determined. In addition, we
propose in the reported paper [7] two disparate
techniques to further enhance the resulting performance
of our multi-predictor-based GP method.As in our
previous GECCO Hot-off-the-Press paper [1], this
abstract paper presents to the GECCO community a
verified application of GP on a more difficult and
challenging type of WS QoS time series forecasting. Our
purpose is to enable the GECCO community to use this
application of GP, to try to improve GP to obtain more
accurate and better results, and to investigate other
potential evolutionary paradigms and techniques for
this issue.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Yang Syu
Yong-Yi FanJiang
Citations