Cross-Domain Workloads Performance Prediction via Runtime Metrics Transferring

Modern cloud vendors provide thousands of types of virtual machines, presenting the user a bewildering choice. Many previous works try to train accurate models for a workload to predict its performance on a given configuration, which could be applicable to a concrete class of workloads (e.g., Spark-SVM jobs). But it is difficult to build a universal prediction model for all the workloads, mostly because different workloads have different runtime metrics distribution. We argue that if the runtime metrics data could be transferred before being fed to the prediction model, the knowledge learned in one workload may be reused to other workloads. In this paper, we synthetically investigate the similarity and difference between a set of workloads and the possibility to transfer between them. We use an LSTM auto-encoder to encode the temporal runtime metrics into a fixed-size vector, which is fed into a linear regression model. In our experiments, the metrics transferring approach could help reduce the prediction error of the linear regression model.