DCCPI-predictor: A state-aware approach for effectively predicting cross-core performance interference

Abstract

Multicore processors are extensively adopted in data center. Applications running on multicore processors may experience performance interference due to the contention for shared resources, which can negatively affect the Qos of online applications and reduce revenue. In order to guarantee the QoS of online applications, data center always over-provision resources for online applications, leaving a large number of cores idle, resulting in extremely low resource utilization. Improving resource utilization while ensuring the Qos of online applications is a challenge issue for data center. Most of the previous work has focused on interference prediction in fixed state mode, which affects its effectiveness in production data center. In this paper, we propose a novel interference prediction approach, namely dCCPI-predictor, which dynamically predicts the cross-core performance interference of multiple applications running together so as to identify the ’safe’ co-locations to share the server resource. dCCPI-predictor builds an interference prediction model for each application that enabling calculate the performance degradation that the application suffers in any co-location. dCCPI-predictor dynamically adapts to the state change of the application, predicting the performance interference in different states, which was overlooked in previous work. We conducted experiments on a simulated data center over multiple benchmarks to evaluate our approach. Results show that dCCPI-predictor can predict performance interference with a very high accuracy, which is greatly superior to static approach.