Статья

SARIMA Techniques for Predictive Resource Provisioning in Cloud Environments

C. Rohith BhatSaveetha School of Engineering (Saveetha Institute of Medical and Technical Sciences),Department of Computer Science and Engineering,Chennai,Tamilnadu,IndiaB. PrabhaA. Cecil DonaldCHRIST(Deemed to be University),Department of Computer Science,Bengaluru,IndiaSwati SahSchool of SET Sharda university,Greater Noida,IndiaHarshal PatilComputer Science and Engineering Symbiosis Institute of Technology, Symbiosis International (Deemed University),Pune,IndiaA. FirosRajiv Gandhi University (A Central University)Rono-Hills,Department of computer Science and Engineering,Doimukh,Arunachal Pradesh,India

2023en

ABI

Аннотация

Seasonal Autoregressive Integrated Moving Average (SARIMA) models for dynamic cloud resource provisioning are introduced and evaluated in this work. Various cloud-based apps provided historical data to train and evaluate SARIMA models. The SARIMA(1,1,1)(0,1,1)12 model has an MAE of 0.056 and an RMSE of 0.082, indicating excellent prediction ability. This model projected resource needs better than other SARIMA settings. Sample prediction vs. real study showed close congruence between projected and observed resource consumption. MAE improved with hyperparameter adjustment, according to sensitivity analysis. Moreover, SARIMA-based resource allocation improved CPU usage by 12.5%, RAM utilization by 20%, and storage utilization by 21.4%. These data demonstrate SARIMA's ability to forecast cloud resource needs. SARIMA-based resource management might change dynamic cloud resource management systems due to cost reductions and resource usage efficiency. This research helps industry practitioners improve cloud-based service performance and cost.

Перевод пока недоступен

Идентификаторы

DOI: 10.1109/iccebs58601.2023.10449163

Цитирования и источники

Цитирований: 6Использованных источников: 0

Показатели — AkademScholar