Enhancing the output of time series forecasting algorithms for cloud resource provisioning

Forecasting the resource consumption of workloads is a frequent approach in the cloud provisioning field. Ideally, such predictions allow obtaining a more accurate scheduling and management of resources in a computing cluster. However, the current approaches fail to properly forecast the future consumption in areas where sudden increases of consumption are present, i.e., spikes. Even, commonly employed metrics lack the ability to properly evaluate sharp behaviours in the traces. This may generate resource starvation problems in the running workloads and decreases the Quality of Service (QoS) provided to external users. To address this issue, we propose two strategies that modify the outputs of forecasting algorithms without changing the algorithms' internals. The new outputs considerably enhance the prediction of sudden increases, duplicating the F1 score metric in average for all tested algorithms. This improvement in the handling of spikes comes with an increased over-provision of resources. Nevertheless, the proposed strategies give the user an easy way to control this trade-off between predicting spikes and the amount of over-provision. The user can decide which is the right balance that better fits the requirements of its specific scenario. Furthermore, we propose a new evaluation methodology that better assesses the behaviour of forecasting algorithms in cloud traces, especially focused on the performance around increases of consumption, and we give insights on the reasons behind the predictions of the algorithms with the application of explainability techniques. The code repository of this work can be accessed through GitHub at this link https://github.com/FerranAgulloLopez/ResourceForecasting.