2026-06-11T04:10:39.498Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:9682 2024-12-03T20:10:47Z 3

Cloud Computing Load Forecasting by Using Bidirectional Long Short-Term Memory Neural Network, Chapter in LNNS Lecture Notes in Networks and Systems: ICICNIS 2023: IoT Based Control Networks and Intelligent Systems, Springer, volume 789 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9682 https://link.springer.com/chapter/10.1007/978-981-99-6586-1_45 M. Salb A. El-sadai L. Jovanovic M. Zivkovic N. Bacanin N. Budimirovic Cloud services play an increasingly significant role in daily life. The widespread integration of the Internet of Things, and online services has increased demand for stable and reliable cloud services. To maximize utilization of available computing power a need for a robust system for forecasting cloud load is evident. This work proposed an artificial intelligence (AI)-based approach applied to cloud load forecasting. By utilizing bidirectional long short-term memory (BiLSTM) neural networks and formulating this task as a time-series forecasting challenge accurate forecasts can be made. However, proper functioning of BiLSTM is very reliant on proper hyper-parameter selection. To select the optimal values suited to this task a modified version of the sine cosine algorithm (SCA) is introduced to optimize the performance of the proposed method. The introduced approach is subjected to a comparative analysis against several contemporary algorithms tested on a real-world data-set. The attained outcomes indicate that the introduced approach has decent potential for forecasting cloud load in a real-world environment. bookPart Springer, Singapore 667 682 10.1007/978-981-99-6586-1_45 LNNS Lecture Notes in Networks and Systems: ICICNIS 2023: IoT Based Control Networks and Intelligent Systems, volume 789