Workload Forecasting Methods in Cloud Environments: An Overview | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Volume 17, Issue 2, December 2023, Pages 29-37 PDF (757.08 K) | ||
| Document Type: Review Paper | ||
| DOI: 10.33899/csmj.2023.181629 | ||
| Authors | ||
| Samah F. Aziz* 1; Manar Y. Kashmoola2 | ||
| 1AlHamdaniya University, Mosul, Iraq | ||
| 2College of Information Technology, Ninevah University, Mosul, Iraq | ||
| Abstract | ||
| Cloud computing is becoming increasingly popular due to its on-demand resource allocation and scalability. It is essential to precisely anticipate workload as applications and users on cloud-based services increase to distribute resources effectively and avoid service interruptions. We present an overview of approaches for workload forecasting in cloud systems in this study. We explore more sophisticated approaches like algorithms for deep learning (DL) and machine learning (ML) in addition to more conventional approaches like analysis of time series and models of regression. We also discuss difficulties and unresolved research questions in the area of workload forecasting for cloud settings. Cloud service providers may allocate resources wisely and guarantee good performance and accessibility for their clients by being aware of these techniques and problems. Cloud computing with virtualization and customized service is crucial to improving the service provided to customers. Accurate forecasting of workload is key to optimizing cloud performance. In this study, we discuss some methods of predicting workload in cloud environments. This study presents an overview of workload prediction techniques in cloud systems, with a special emphasis on long short-term memory (LSTM) networks. We go through the fundamental ideas behind LSTM networks and how well they can detect long-term relationships in data from time series. We also examine the particular difficulties and factors involved in LSTM-based workload forecasting implementation in cloud systems. We also examine previous research and methods that have employed LSTM networks to forecast workload in cloud systems. We examine the benefits and drawbacks of different methods, focusing on their effectiveness, scalability, and interpretability. | ||
| Keywords | ||
| long short term memory (LSTM); machine learning (ML); deep learning (DL); neural networks; review; cloud computing; workload forecasting | ||
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