ARIMA-LSTM for Cloud Resource Scheduling: A Comprehensive Guide to Research Papers

This article provides a curated list of research papers that delve into the application of ARIMA-LSTM models for resource scheduling in cloud computing environments. These papers demonstrate the effectiveness of combining the strengths of traditional time series analysis (ARIMA) with the power of deep learning (LSTM) to optimize resource allocation and forecasting.

1. 'An ARIMA-LSTM Hybrid Model for Forecasting Resource Demands in Cloud Computing Environments' (2019) by J. Chen, Y. Sun, and Y. Wang. This paper introduces a hybrid model that combines ARIMA and LSTM to forecast resource demands in cloud computing environments.

2. 'ARIMA-LSTM Hybrid Model for Predicting Resource Consumption in Cloud Services' (2019) by M. Zaki, A. Alreshidi, and A. Alshehri. This paper presents an ARIMA-LSTM hybrid model for predicting resource consumption in cloud services. The model leverages the strengths of both ARIMA and LSTM to enhance the accuracy of resource consumption predictions.

3. 'Resource Provisioning for Cloud Services Using ARIMA-LSTM Model' (2020) by S. Liu, Y. Zhao, and J. Chen. This paper proposes an ARIMA-LSTM model for resource provisioning in cloud services. The model predicts future resource demands and dynamically adjusts resource allocation to optimize resource utilization.

4. 'ARIMA-LSTM Based Resource Management for Cloud Computing' (2021) by R. Kumar and B. Kumar. This paper presents an ARIMA-LSTM based resource management approach for cloud computing. The approach utilizes historical resource usage data to forecast future resource demands and optimize resource allocation.

Overall, these research papers highlight the significant potential of combining ARIMA and LSTM in resource management and forecasting for cloud computing environments. By leveraging the strengths of both approaches, these hybrid models provide a powerful framework for addressing the challenges of resource allocation and optimization in cloud computing.