Proceedings of the 2016 ACM/Spec International Conference on Performance Engineering (Icpe'16)
We present a tool, PROST, which can achieve scalable and accurate prediction of server workload time series in data centers. As several virtual machines are typically co-located on physical servers, the CPU and RAM show strong temporal and spatial dependencies. PROST is able to leverage the spatial dependency among co-located VMs to improve the scalability of prediction models solely based on temporal features, such as neural network. We show the benefits of PROST in obtaining accurate prediction of resource usage series and designing effective VM sizing strategies for the private data centers.
Xue, J., Smirni, E., Scherer, T., Birke, R., & Chen, L. Y. (2016, March). PROST: Predicting Resource Usages with Spatial and Temporal Dependencies. In Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering (pp. 125-126). ACM.