ARIMA-LSTM Model for Efficient Cloud Computing Resource Allocation

Cloud computing has become an essential technology trend in today's digital landscape. Effective resource allocation is a crucial aspect of cloud computing, ensuring optimal performance and cost efficiency. This research presents a novel method for cloud computing resource allocation based on the ARIMA-LSTM model, combining the strengths of both ARIMA and LSTM models.

The proposed ARIMA-LSTM model is designed to accurately predict cloud workload, enabling efficient resource allocation. Experimental results demonstrate that this method outperforms traditional methods in terms of prediction accuracy and resource utilization.

Key Contributions:

Combined Model: This research introduces a hybrid ARIMA-LSTM model for cloud workload prediction, leveraging the advantages of both models.
Resource Allocation Optimization: The model effectively optimizes resource allocation based on accurate workload predictions.
Improved Performance: Experimental results demonstrate significant improvements in prediction accuracy and resource utilization compared to traditional methods.

This research offers valuable insights into the application of hybrid machine learning models for efficient cloud resource management.

Source:

Title: Resource Allocation for Cloud Computing Using ARIMA-LSTM Model
Authors: Xian Zhang, Meng Zhang, Xiaojun Zhu, Yan Ma
Publication: 2019 IEEE 5th International Conference on Computer and Communications (ICCC)
Link: https://ieeexplore.ieee.org/document/8939081