Tail Prediction for Heterogeneous Data Center Clusters

Abstract

Service providers need to meet their service level objectives (SLOs) to ensure better client experiences. Predicting tail sojourn times of applications is an essential step to combat long tail latency. Therefore, as an attempt to further unravel the power of our prediction model, new study scenarios for heterogeneous environments will be introduced in this research by using either of two methods: white- or black-box solutions. This research presents several techniques for modeling clusters of inhomogeneous nodes. Those techniques are recognized as heterogeneous fork-join queuing networks (HFJQNs). Moreover, included in the research is a nested-event-based simulation model, borrowing help from multi-core technologies. This model adopts the multiprocessing technique to take part in its design to enable different architectural designs for all computing nodes. This novel implementation of the simulation model is believed to be the next logical step for research studies targeting heterogeneous clusters in addition to the several provided scenarios. Experimental results confirm that even with the existence of such heterogeneous conditions, the tail latency can be predicted at high-load regions with an approximated relative error of less than 15%.