Function Memory Optimization for Heterogeneous Serverless Platforms with CPU Time Accounting

Serverless Function-as-a-Service (FaaS) platforms often abstract the underlying infrastructure configuration into the single option of specifying a function's memory reservation size. This resource abstraction of coupling configurations options (e.g. vCPUs, memory, disk), combined with the lack of profiling, leaves developers to make ad hoc decisions on how to configure functions. Solutions are needed to mitigate exhaustive brute force searches of large parameter input spaces to find optimal configurations which can incur high costs. To address these challenges, we propose CPU Time Accounting Memory Selection (CPU-TAMS). CPU-TAMS is a workload agnostic memory selection method that utilizes CPU time accounting principles and regression modeling to recommend memory settings that reduce function runtime and subsequently, cost. Comparing CPU-TAMS to eight existing selection methods, we find that CPU-TAMS finds maximum value memory settings with only 8% runtime and 5% cost error compared to brute force testing while only requiring a single profiling run to evaluate function resource requirements. We adapt CPU-TAMS for use on four commercial FaaS platforms demonstrating efficacy to optimize function memory configurations where platforms feature heterogeneous infrastructure management policies.