Cost-Optimised IEEE/ACM UCC 2024

MemFigLess

An input-aware ensemble learning method — Multi-output Random Forest — for dynamic memory configuration of serverless functions. Achieves 57–87% cost savings and 54–82% memory allocation reduction vs. static baselines on AWS Lambda. Evaluated against COSE, Parrotfish, and AWS Power Tuning.

AWS Lambda Python

Python

Scikit-learn

DynamoDB

GitHub IEEE Xplore

Results

Performance

57–87%

Run-time cost savings

54–82%

Memory allocation reduction

R² 98%

Execution time prediction accuracy

R² 96%

Memory utilisation prediction accuracy

Evaluated against COSE, Parrotfish, and AWS Power Tuning across diverse serverless function benchmarks.

Design

MAPE Control Loop

Monitor — Offline Training

Profiles serverless functions across diverse input sizes to build a labelled dataset of input characteristics, execution times, and memory consumption. Data stored in AWS DynamoDB and S3.

Analyse — Random Forest Model

Multi-output Random Forest Regressor learns input-to-resource correlations, jointly predicting execution time and memory utilisation for any unseen input payload.

Plan — Constraint Optimisation

Given model predictions, solves a constraint optimisation problem to select the minimum-cost memory configuration that satisfies execution time and SLA constraints.

Execute — Dynamic Configuration

Reconfigures AWS Lambda memory allocation per invocation via the AWS API before function execution. Periodic model retraining via AWS Step Functions maintains accuracy under workload drift.

Evaluation

Benchmark Functions

Mathematical Functions

Matrix multiplication, linear algebra (Linpack), and cryptographic operations (pyaes). Up to 73% resource savings.

matmul linpack pyaes

Graph Algorithms

Minimum spanning tree, breadth-first search, and PageRank. Up to 87% cost efficiency.

graph-mst graph-bfs pagerank

Web & Media Processing

Dynamic HTML generation and template rendering workloads with significant performance improvements.

chameleon dynamic-html

Research

Publication

IEEE/ACM UCC 2024 · pp. 346–355

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

Agarwal, S. et al. — IEEE CS Press · Sharjah, UAE · DOI: 10.1109/UCC62667.2024

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