Understanding, Predicting and Scheduling Serverless Workloads Under Partial Interference

Authors: Laiping Zhao, Yanan Yang, Yiming Li, Xian Zhou, and Keqiu Li (Tianjin University, China)

Abstract: Interference among distributed cloud applications can be classified into three types: full, partial and zero. While prior research merely focused on full interference, the partial interference that occurs at parts of applications is far more common yet still lacks in-depth study. Serverless computing that structures applications into small-sized, short-lived functions further exacerbate partial interference. We characterize the features of partial interference in serverless as exhibiting high volatility, spatial-temporal variation, and propagation. Given these observations, we propose an incremental learning predictor, named Gsight, which can achieve high precision by harnessing the spatial-temporal overlap codes and profiles of functions via an end-to-end call path. Experimental results show that Gsight can achieve an average error of 1.71%. Its convergence speed is at least 3x faster than that in a serverful system. A scheduling case study shows that the proposed method can improve function density by >18.79% while guaranteeing the quality of service (QoS).


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