Abstract
The importance of cloud computing has been rapidly growing due to the increasing number of users’ requests for diverse sets of resources. Although clouds have rich resources to handle these incoming requests, under or over-provisioning of resources can lead to failure. Therefore, it is important to provision cloud resources appropriately. Machine-learning based techniques have been proven to be effective in the management of resources along with maintaining a Service Level Agreement (SLA). These techniques require complete data to produce better prediction results. In practice, it may happen that the data is incomplete and data with more missing attribute values can negatively affect the outcome of the predictions. Therefore, interpolation of missing attribute values is crucial for better predictions. However, the existing methods for interpolation of missing attribute values are heavy in terms of computation. This paper first predicts resource usage in terms of CPU by applying the lightGBM model to a real dataset. Furthermore, using the explanations of SHapley Additive exPlanations (SHAP) in combination with the K-Nearest Neighbor (KNN) to interpolate missing values in the dataset for CPU usage prediction. The experimental results show that SHAP explanations can be helpful for cloud providers in the selection of important features for interpolation of missing values. This SHAP-based interpolation results in lower computational time along with acceptable accuracy in comparison with KNN-based interpolation.
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Fahimullah, M., Gupta, R., Ahvar, S., Trocan, M. (2022). Explaining Predictive Scheduling in Cloud. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_7
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