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Sizeless: Pbkp_redicting the Optimal Size of Serverless Functions

Abstract:

Serverless functions are an emerging cloud computing paradigm that is being rapidly adopted by both industry and academia. In this cloud computing model, the provider opaquely handles resource management tasks such as resource provisioning, deployment, and auto-scaling. The only resource management task that developers are still in charge of is selecting how much resources are allocated to each worker instance. However, selecting the optimal size of serverless functions is quite challenging, so developers often neglect it despite its significant cost and performance benefits. Existing approaches aiming to automate serverless functions resource sizing require dedicated performance tests, which are time-consuming to implement and maintain. In this paper, we introduce an approach to pbkp_redict the optimal resource size of a serverless function using monitoring data from a single resource size. As our approach does not require dedicated performance tests, it enables cloud providers to implement resource sizing on a platform level and automate the last resource management task associated with serverless functions. We evaluate our approach on four different serverless applications on AWS, where it pbkp_redicts the execution time of the other memory sizes based on monitoring data for a single memory size with an average pbkp_rediction error of 15.3%. Based on these pbkp_redictions, it selects the optimal memory size for 79.0% of the serverless functions and the second-best memory size for 12.3% of the serverless functions, which results in an average speedup of 39.7% while also decreasing average costs by 2.6%.