# Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless Computing Functions

arXiv: https://arxiv.org/abs/2411.07444 · HTML: https://mlsanity.com/paper/2411.07444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing](https://mlsanity.com/paper/2012.14132/) (2020)
- [Sizeless: Predicting the optimal size of serverless functions](https://mlsanity.com/paper/2010.15162/) (2020)
- [With Great Freedom Comes Great Opportunity: Rethinking Resource Allocation for Serverless Functions](https://mlsanity.com/paper/2105.14845/) (2021)
- [SLAM: SLO-Aware Memory Optimization for Serverless Applications](https://mlsanity.com/paper/2207.06183/) (2022)

## Abstract (arXiv)

In today's Function-as-a-Service offerings, a programmer is usually responsible for configuring function memory for its successful execution, which allocates proportional function resources such as CPU and network. However, right-sizing the function memory force developers to speculate performance and make ad-hoc configuration decisions. Recent research has highlighted that a function's input characteristics, such as input size, type and number of inputs, significantly impact its resource demand, run-time performance and costs with fluctuating workloads. This correlation further makes memory configuration a non-trivial task. On that account, an input-aware function memory allocator not only improves developer productivity by completely hiding resource-related decisions but also drives an opportunity to reduce resource wastage and offer a finer-grained cost-optimised pricing scheme. Therefore, we present MemFigLess, a serverless solution that estimates the memory requirement of a serverless function with input-awareness. The framework executes function profiling in an offline stage and trains a multi-output Random Forest Regression model on the collected metrics to invoke input-aware optimal configurations. We evaluate our work with the state-of-the-art approaches on AWS Lambda service to find that MemFigLess is able to capture the input-aware resource relationships and allocate upto 82% less resources and save up to 87% run-time costs.