2026-05-05T12:42:57.184Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:9708 2024-12-03T20:10:47Z 1

Decomposition aided cloud load forecasting with optimized long-short term memory networks 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/9708 https://ieeexplore.ieee.org/abstract/document/10316036 N. Bacanin L. Jovanovic M. Zivkovic M. Salb A. Elsadai M. Sarac Cloud load forecasting plays an important role in information technology. There is the increasing reliance on online services for accurately predicting service demand in order to take timely actions and guarantee better services and greater customer satisfaction. However, the complexities associated with making accurate forecasts make this a challenging task. By formulating this task as a time series forecasting problem powerful artificial intelligence algorithms able to handle temporal data such as the long short-term memory (LSTM) neural network may be used for this problem. Additionally, to help tackle the complex nature of the data, variational mode decomposition is incorporated into the approach. Better accuracy can be obtained by sleeting appropriate LSTM control parameters. Several well-known metaheuristics have been evaluated on their ability to optimize LSTM network parameters and improve performance when applied to a real-world cloud load forecasting dataset. The metaheuristic algorithms are subjected to comparative analysis to determine the best-performing approach. conferenceObject 191 194 10.1109/TELSIKS57806.2023.10316036 2023 16th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), IEEE, Nis, Serbia