Understanding, predicting and scheduling serverless workloads under partial interference
Laiping Zhao, Yanan Yang, Yiming Li, Xian Zhou, Keqiu Li
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).