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Host load prediction is essential for dynamic resource scaling and jobscheduling in a cloud computing environment. In this context, workloadprediction is challenging because of several issues. First, it must be accurateto enable precise scheduling decisions. Second, it must be fast to schedule atthe right time. Third, a model must be able to account for new patterns ofworkloads so it can perform well on the latest and old patterns. Not being ableto make an accurate and fast prediction or the inability to predict new usagepatterns can result in severe outcomes such as service level agreement (SLA)misses. Our research trains a fast model with the ability of online adaptationbased on the gated recurrent unit (GRU) to mitigate the mentioned issues. Weuse a multivariate approach using several features, such as memory usage, CPUusage, disk I/O usage, and disk space, to perform the predictions accurately.Moreover, we predict multiple steps ahead, which is essential for makingscheduling decisions in advance. Furthermore, we use two pruning methods: L1norm and random, to produce a sparse model for faster forecasts. Finally,online learning is used to create a model that can adapt over time to newworkload patterns.

An Efficient Online Prediction of Host Workloads Using Pruned GRU Neural Nets