References
- Herman, M., Iorga, M., Salim, A., Jackson, R., Hurst, M., Leo, R., Mishra, A., Landreville, N. and Wang, Y. NIST Cloud Computing Forensic Reference Architecture. (National Institute of Standards,2023)
- Osypanka, P. and Nawrocki, P. Resource Usage Cost Optimization in Cloud Computing Using Machine Learning. IEEE Transactions On Cloud Computing. pp. 1-1 (2020)
- Mason, K., Duggan, M., Barrett, E., Duggan, J. and Howley, E. Predicting host CPU utilization in the cloud using evolutionary neural networks. Future Generation Computer Systems. 86 pp. 162-173 (2018)
- Chen, J. and Wang, Y. A Resource Demand Prediction Method Based on EEMD in Cloud Computing. Procedia Computer Science. 131 pp. 116-123 (2018), Recent Advancement in Information and Communication Technology:
- Chen, H., Fu, X., Tang, Z. and Zhu, X. Resource Monitoring and Prediction in Cloud Computing Environments. 2015 3rd International Conference On Applied Computing And Information Technology/2nd International Conference On Computational Science And Intelligence. pp. 288-292 (2015).
- Nguyen, T., Tran, N., Nguyen, B. and Nguyen, G. A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. 2018 IEEE 11th Conference On Service-Oriented Computing And Applications (SOCA). pp. 49-56 (2018).
- Nawrocki, P., Grzywacz, M. and Sniezynski, B. Adaptive resource planning for cloud-based services using machine learning. Journal Of Parallel And Distributed Computing. 152 pp. 88-97 (2021).
- Kumar, J., Goomer, R. and Singh, A. Long Short Term Memory Recurrent Neural Network (LSTMRNN) Based Workload Forecasting Model For Cloud Datacenters. Procedia Computer Science. 125 pp. 676-682 (2018), The 6th International Conference on Smart Computing and Communications.
- Osypanka, P. and Nawrocki, P. QoS-aware Cloud Resource Prediction for Computing Services. IEEE Transactions On Services Computing. pp. 1-1 (2022).
- Gupta, S. and Dinesh, D. Online adaptation models for resource usage prediction in cloud network. 2017 Twenty-third National Conference On Communications (NCC). pp. 1-6 (2017).
- Kumar, J. and Singh, A. Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Generation Computer Systems. 81 pp. 41-52 (2018).
- Sniezynski, B., Nawrocki, P., Wilk, M., Jarzab, M. and Zielinski, K. VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing. Journal Of Grid Computing. 17 pp. 797-812 (2019).
- Gupta, S., Dileep, A. and Gonsalves, T. Online Sparse BLSTM Models for Resource Usage Prediction in Cloud Datacentres. IEEE Transactions On Network And Service Management. 17, 2335-2349 (2020).
- Fang, Z., Ma, X., Pan, H., Yang, G. and Arce, G. Movement forecasting of financial time series based on adaptive LSTM-BN network. Expert Systems With Applications. 213 pp. 119207 (2023).
- Gasparin, A., Lukovic, S. and Alippi, C. Deep learning for time series forecasting: The electric load case. CAAI Transactions On Intelligence Technology. 7, 1-25 (2022).
- Nawrocki, P. and Sus, W. Anomaly detection in the context of long-term cloud resource usage planning. Knowledge And Information Systems. 64, 2689-2711 (2022,10).
- A., A. Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning. International Journal Of Advanced Computer Science And Applications. 7 (2016).
- Dixit, A., Gupta, R., Dubey, A. and Misra, R. Machine Learning Based Adaptive Auto-scaling Policy for Resource Orchestration in Kubernetes Clusters. Internet Of Things And Connected Technologies. pp. 1-16 (2022).
- Salles, R., Belloze, K., Porto, F., Gonzalez, P. and Ogasawara, E. Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems. 164 pp. 274-291 (2019).
- Musbah, H., Aly, H. and Little, T. A proposed novel adaptive DC technique for non-stationary data removal. Heliyon. 9 (2023).
- Ben Taieb, S., Bontempi, G., Atiya, A. and Sorjamaa, A. A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Systems With Applications. 39, 7067-7083 (2012).
- González-Sope˜na, J., Pakrashi, V. and Ghosh, B. An overview of performance evaluation metrics for short-term statistical wind power forecasting. Renewable And Sustainable Energy Reviews. 138 pp. 110515 (2021).
- Bergmeir, C., Hyndman, R. and Koo, B. A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis. 120 pp. 70-83 (2018).
- Li, X., Cao, J., Guo, J., Liu, C., Wang, W., Jia, Z. and Su, T. Multi-step forecasting of ocean wave height using gate recurrent unit networks with multivariate time series. Ocean Engineering. 248 pp. 110689 (2022).
- Carletti, M., Terzi, M. and Susto, G. Interpretable Anomaly Detection with DIFFI: Depth-based feature importance of Isolation Forest. Engineering Applications Of Artificial Intelligence. 119 pp. 105730 (2023).
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, Ł. and Polosukhin, I. Attention is All you Need. Advances In Neural Information Processing Systems. 30 (2017).
- Giannakas, F., Troussas, C., Krouska, A., Sgouropoulou, C. and Voyiatzis, I. XGBoost and Deep Neural Network Comparison: The Case of Teams’ Performance. Intelligent Tutoring Systems. pp. 343-349 (2021).
- Arik, S., Yoder, N. and Pfister, T. Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series. CoRR. abs/2202.02403 (2022).
- Zeng, A., Chen, M., Zhang, L. and Xu, Q. Are Transformers Effective for Time Series Forecasting? (arXiv,2022).
- Gárate-Escamila, A., Hajjam El Hassani, A. and Andrès, E. Classification models for heart disease prediction using feature selection and PCA. Informatics In Medicine Unlocked. 19 pp. 100330 (2020).
- Lim, B., Arık, S., Loeff, N. and Pfister, T. Temporal Fusion Transformers for interpretable multi-horizon time series forecasting. International Journal Of Forecasting. 37, 1748-1764 (2021).
- Canela, M., Alegre, I. and Ibarra, A. Holt-Winters Forecasting. Quantitative Methods For Management: A Practical Approach. pp. 121-128 (2019).
- Singarimbun, R., Nababan, E. and Sitompul, O. Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm. 2019 International Conference Of Computer Science And Information Technology (ICoSNIKOM). pp. 1-7 (2019).
- Jayalath, J., Chathumali, E., Kothalawala, K. and Kuruwitaarachchi, N. Green Cloud Computing: A Review on Adoption of Green-Computing attributes and Vendor Specific Implementations. 2019 International Research Conference On Smart Computing And Systems Engineering (SCSE). pp. 158-164 (2019).