Shallow, Deep, Ensemble models for Network Device Workload Forecasting

• Autorzy: Liu Cenru

Identyfikatory

DOI

10.15439/2020F137

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Reliable prediction of workload-related characteristics of monitored devices is important and helpful for management of infrastructure capacity. This paper presents 3 machine learning models (shallow, deep, ensemble) with different complexity for network device workload forecasting. The performance of these models have been compared using the data provided in FedCSIS'20 Challenge. The R2 scores achieved from the cascade Support Vector Regression (SVR) based shallow model, Long short-term memory (LSTM) based deep model, and hierarchical linear weighted ensemble model are 0.2506, 0.2831, and 0.3059, respectively, and was ranked 3rd place in the preliminary stage of the challenges.

Słowa kluczowe

EN

workload forecasting; recurrent neural network; RNN; long short-term memory; LSTM; support vector regression; SVR; hierarchical linear weighted ensemble

PL

prognozowanie obciążenia; rekurencyjna sieć neuronowa; RNN; pamięć krótkotrwała; LSTM; regresja wektora nośnego; SVR

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 101--104
• Opis fizyczny: Bibliogr. 30 poz., wz., rys.

Twórcy

autor

Liu Cenru

• Ngee Ann Polytechnic, Singapore

Bibliografia

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Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).