TY - GEN
T1 - A Deep Learning Approach for Amazon EC2 Spot Price Prediction
AU - Al-Theiabat, Hana
AU - Al-Ayyoub, Mahmoud
AU - Alsmirat, Mohammad
AU - Aldwair, Monther
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Spot Instances (SI) represent one of the ways cloud service providers use to deal with idle resources in off-peak periods, where these resources are being auctioned at low prices to customers with limited budgets in a dynamic manner. However, SI are poorly utilized due to issues like out-of-bid failures and bidding complexity. Thus, effective SI price models are of great importance to customers in order to plan their bidding strategies. This paper proposes a deep learning approach for Amazon EC2 SI price prediction, which is a time-series analysis (TSA) problem. The proposed Long Short-Term Memory (LSTM) approach is compared with a well-known classical (i.e., non deep learning) approach for TSA, which is AutoRegressive Integrated Moving Average (ARIMA), using different accuracy measures commonly used in TSA. The results show the superiority of the LSTM approach compared with the ARIMA approach in many aspects.
AB - Spot Instances (SI) represent one of the ways cloud service providers use to deal with idle resources in off-peak periods, where these resources are being auctioned at low prices to customers with limited budgets in a dynamic manner. However, SI are poorly utilized due to issues like out-of-bid failures and bidding complexity. Thus, effective SI price models are of great importance to customers in order to plan their bidding strategies. This paper proposes a deep learning approach for Amazon EC2 SI price prediction, which is a time-series analysis (TSA) problem. The proposed Long Short-Term Memory (LSTM) approach is compared with a well-known classical (i.e., non deep learning) approach for TSA, which is AutoRegressive Integrated Moving Average (ARIMA), using different accuracy measures commonly used in TSA. The results show the superiority of the LSTM approach compared with the ARIMA approach in many aspects.
KW - Amazon EC2 Spot Instance Price Prediction
KW - AutoRegressive Integrated Moving Average (ARIMA)
KW - Long Short-Term Memory (LSTM)
KW - Time-Series Analysis
UR - https://www.scopus.com/pages/publications/85061917036
U2 - 10.1109/AICCSA.2018.8612783
DO - 10.1109/AICCSA.2018.8612783
M3 - Conference contribution
AN - SCOPUS:85061917036
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018
PB - IEEE Computer Society
T2 - 15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018
Y2 - 28 October 2018 through 1 November 2018
ER -