Scholarlink

<p>Cloud services operate in highly dynamic and heterogeneous environments, requiring continuous and accurate assessment of service quality. While Quality of Service (QoS) models are widely used to monitor performance, deep learning (DL) architectures–such as Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Units (BI-GRU)–offer enhanced capabilities for forecasting potential Service Level Agreement (SLA) violations. However, many existing experiments in this domain suffer from methodological shortcomings, including the use of outdated or proprietary datasets, a narrow set of QoS metrics, incomplete documentation of model architectures and training procedures, and a lack of statistical rigor, which undermines reproducibility and applicability in industrial contexts. This study empirically compares the performance of BI-GRU, LSTM, and AutoRegressive Integrated Moving Average (ARIMA) models for QoS forecasting using a rigorously designed experimental protocol that addresses these limitations. We build a multi-metric QoS dataset covering five months of operational data from a cloud service in an IT company, comprising 16 QoS metrics. Forecasting models were trained and evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with training time considered as an efficiency indicator. BI-GRU outperformed ARIMA across all QoS metrics and achieved statistically significant improvements over LSTM in 9 out of 16 metrics. In contrast, LSTM only significantly outperformed BI-GRU in one metric. Our findings demonstrate that most BI-GRU models provide superior accuracy and efficiency. Furthermore, the methodological rigor of the experimental design supports their applicability for proactive QoS management and informed decision-making in industrial cloud service environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Building and validating deep learning models for forecasting the quality of cloud services

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

查看全文
  • Ximena Guerron,
  • Marta Fernández-Diego,
  • Silvia Abrahão,
  • Emilio Insfran,
  • Sira Vegas

摘要

Cloud services operate in highly dynamic and heterogeneous environments, requiring continuous and accurate assessment of service quality. While Quality of Service (QoS) models are widely used to monitor performance, deep learning (DL) architectures–such as Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Units (BI-GRU)–offer enhanced capabilities for forecasting potential Service Level Agreement (SLA) violations. However, many existing experiments in this domain suffer from methodological shortcomings, including the use of outdated or proprietary datasets, a narrow set of QoS metrics, incomplete documentation of model architectures and training procedures, and a lack of statistical rigor, which undermines reproducibility and applicability in industrial contexts. This study empirically compares the performance of BI-GRU, LSTM, and AutoRegressive Integrated Moving Average (ARIMA) models for QoS forecasting using a rigorously designed experimental protocol that addresses these limitations. We build a multi-metric QoS dataset covering five months of operational data from a cloud service in an IT company, comprising 16 QoS metrics. Forecasting models were trained and evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with training time considered as an efficiency indicator. BI-GRU outperformed ARIMA across all QoS metrics and achieved statistically significant improvements over LSTM in 9 out of 16 metrics. In contrast, LSTM only significantly outperformed BI-GRU in one metric. Our findings demonstrate that most BI-GRU models provide superior accuracy and efficiency. Furthermore, the methodological rigor of the experimental design supports their applicability for proactive QoS management and informed decision-making in industrial cloud service environments.