Workload Forecasting Methods in Cloud Environments: An Overview
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 ov...
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Mosul University
2023-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
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| Online Access: | https://csmj.mosuljournals.com/article_181629_26fa7691b5ca9af20988faf8df36e211.pdf |
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| author | Samah Aziz Manar Kashmoola |
| author_facet | Samah Aziz Manar Kashmoola |
| author_sort | Samah Aziz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-be3b4b71b55a450dbfe9133cddf3c2a4 |
| institution | OA Journals |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-be3b4b71b55a450dbfe9133cddf3c2a42025-08-20T02:02:44ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902023-12-01172293710.33899/csmj.2023.181629181629Workload Forecasting Methods in Cloud Environments: An OverviewSamah Aziz0Manar Kashmoola1AlHamdaniya University, Mosul, IraqCollege of Information Technology, Ninevah University, Mosul, IraqCloud 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.https://csmj.mosuljournals.com/article_181629_26fa7691b5ca9af20988faf8df36e211.pdflong short term memory (lstm)machine learning (ml)deep learning (dl)neural networksreviewcloud computingworkload forecasting |
| spellingShingle | Samah Aziz Manar Kashmoola Workload Forecasting Methods in Cloud Environments: An Overview Al-Rafidain Journal of Computer Sciences and Mathematics long short term memory (lstm) machine learning (ml) deep learning (dl) neural networks review cloud computing workload forecasting |
| title | Workload Forecasting Methods in Cloud Environments: An Overview |
| title_full | Workload Forecasting Methods in Cloud Environments: An Overview |
| title_fullStr | Workload Forecasting Methods in Cloud Environments: An Overview |
| title_full_unstemmed | Workload Forecasting Methods in Cloud Environments: An Overview |
| title_short | Workload Forecasting Methods in Cloud Environments: An Overview |
| title_sort | workload forecasting methods in cloud environments an overview |
| topic | long short term memory (lstm) machine learning (ml) deep learning (dl) neural networks review cloud computing workload forecasting |
| url | https://csmj.mosuljournals.com/article_181629_26fa7691b5ca9af20988faf8df36e211.pdf |
| work_keys_str_mv | AT samahaziz workloadforecastingmethodsincloudenvironmentsanoverview AT manarkashmoola workloadforecastingmethodsincloudenvironmentsanoverview |