## Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

### Alexander Acker, Thorsten Wittkopp, Sasho Nedelkoski, Jasmin Bogatinovski, Odej Kao

DOI: http://dx.doi.org/10.15439/2020F149

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 7–10 (2020)

Abstract. The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. This allows different ahead of time optimizations like anomaly detection or predictive maintenance. Predicting the future progress of KPIs can be defined as a time series forecasting problem. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be modelled by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The lightweight nature allows to train models individually on each KPI series and makes model retraining feasible when system changes occur. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall R^2 score of 0.10 on the preliminary 10\% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed\_challenge

### References

- P. A. Dinda and D. R. O’Hallaron, “Host load prediction using linear models,” Cluster Computing, vol. 3, no. 4, pp. 265–280, 2000.
- S. Di, D. Kondo, and W. Cirne, “Host load prediction in a google compute cloud with a bayesian model,” in SC’12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. IEEE, 2012, pp. 1–11.
- B. Song, Y. Yu, Y. Zhou, Z. Wang, and S. Du, “Host load prediction with long short-term memory in cloud computing,” The Journal of Supercomputing, vol. 74, no. 12, pp. 6554–6568, 2018.
- F. Schmidt, F. Suri-Payer, A. Gulenko, M. Wallschläger, A. Acker, and O. Kao, “Unsupervised anomaly event detection for vnf service monitoring using multivariate online arima,” in 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2018, pp. 278–283.
- J. W. Jiang, T. Lan, S. Ha, M. Chen, and M. Chiang, “Joint vm placement and routing for data center traffic engineering,” in 2012 Proceedings IEEE INFOCOM. IEEE, 2012, pp. 2876–2880.
- A. Howard, A. Zhmoginov, L.-C. Chen, M. Sandler, and M. Zhu, “Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation,” 2018.
- F. Ahmad and T. Vijaykumar, “Joint optimization of idle and cooling power in data centers while maintaining response time,” ACM Sigplan Notices, vol. 45, no. 3, pp. 243–256, 2010.
- M. Yaseen, D. Swathi, and T. A. Kumar, “Iot based condition monitoring of generators and predictive maintenance,” in 2017 2nd International Conference on Communication and Electronics Systems (ICCES). IEEE, 2017, pp. 725–729.
- J. H. Stock and M. W. Watson, “A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series,” National Bureau of Economic Research, Tech. Rep., 1998.
- M. R. Hassan and B. Nath, “Stock market forecasting using hidden markov model: a new approach,” in 5th International Conference on Intelligent Systems Design and Applications (ISDA’05). IEEE, 2005, pp. 192–196.
- B. Lim, S. O. Arik, N. Loeff, and T. Pfister, “Temporal fusion transformers for interpretable multi-horizon time series forecasting,” arXiv preprint https://arxiv.org/abs/1912.09363, 2019.
- G. P. Zhang, “Time series forecasting using a hybrid arima and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
- X. Qiu, Y. Ren, P. N. Suganthan, and G. A. Amaratunga, “Empirical mode decomposition based ensemble deep learning for load demand time series forecasting,” Applied Soft Computing, vol. 54, pp. 246–255, 2017.
- A. Janusz, M. Przyborowski, P. Biczyk, and D. Slezak, “Network Device Workload Prediction: A Data Mining Challenge at Knowledge Pit,” in Proceedings of FedCSIS 2020, Sofia, Bulgaria, 2020.
- S. Nedelkoski, J. S. Cardoso, and O. Kao, “Anomaly detection and classification using distributed tracing and deep learning.” in CCGRID, 2019, pp. 241–250.