Document Type
Article
Department/Program
Computer Science
Journal Title
Proceedings of the 2016 ACM/Spec International Conference on Performance Engineering (Icpe'16)
Pub Date
2016
First Page
125
Abstract
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.
Recommended Citation
Xue, Ji; Smirni, Evgenia; Scherer, Thomas; Birke, Robert; and Chen, Lydia Y., PROST: Predicting Resource Usages with Spatial and Temporal Dependencies (2016). Proceedings of the 2016 ACM/Spec International Conference on Performance Engineering (Icpe'16).
10.1145/2851553.2858678
DOI
10.1145/2851553.2858678
