Descriptive and Predictive Analysis of Aggregating Functions in Serverless Clouds: the Case of Video Streaming

TL;DR

This paper investigates how task merging in serverless clouds, especially for video processing, can save time, and develops a machine learning method to accurately estimate these savings for better scheduling.

Contribution

It introduces a benchmarking approach and a GBDT-based model to predict execution-time savings from task merging in serverless video processing.

Findings

Merging can save up to 44% in execution time.

The GBDT model estimates time-saving with an RMSE of 0.04.

Benchmarking reveals the intractability of all merging cases.

Abstract

Serverless clouds allocate multiple tasks (e.g., micro-services) from multiple users on a shared pool of computing resources. This enables serverless cloud providers to reduce their resource usage by transparently aggregate similar tasks of a certain context (e.g., video processing) that share the whole or part of their computation. To this end, it is crucial to know the amount of time-saving achieved by aggregating the tasks. Lack of such knowledge can lead to uninformed merging and scheduling decisions that, in turn, can cause deadline violation of either the merged tasks or other following tasks. Accordingly, in this paper, we study the problem of estimating execution-time saving resulted from merging tasks with the example in the context of video processing. To learn the execution-time saving in different forms of merging, we first establish a set of benchmarking videos and examine a wide variety of video processing tasks -- with and without merging in place. We observed that although merging can save up to 44% in the execution-time, the number of possible merging cases is intractable. Hence, in the second part, we leverage the benchmarking results and develop a method based on Gradient Boosting Decision Tree (GBDT) to estimate the time-saving for any given task merging case. Experimental results show that the method can estimate the time-saving with the error rate of 0.04, measured based on Root Mean Square Error (RMSE).