What question did this study set out to answer?

This study aims to reproduce and extend the capabilities of the CASR scheduler for serverless computing environments.

May 14, 20260 citationsOpen Access

Reproduction and Extension of CASR: A Cache-Based Adaptive Scheduler for Serverless Computing with Novel K=4 Queue Granularity Analysis

Key Points

This study aims to reproduce and extend the capabilities of the CASR scheduler for serverless computing environments.
Implemented CASR in Python with W-TinyLFU caching and PPO reinforcement learning agent.
Evaluated CASR using the Microsoft Azure Functions 2019 dataset with 1,332,032 daily invocations.
Comparison made against five baseline algorithms across three distinct workload types.
CASR eliminates wasted memory time across all evaluated workloads.
Cold start rate reduced by 3.848 to 14.929 percentage points compared to FaaSCache.
The new K=4 design reduces cold start rate by up to 5.900 percentage points over original K=3.

Abstract

This paper presents an independent reproduction and experimental extension of CASR (Cache-Based Adaptive Scheduler for Serverless Runtime), originally proposed by Chen et al. in Future Generation Computer Systems 2025. We implement CASR from scratch in Python including W-TinyLFU caching and PPO reinforcement learning agent. We evaluate on the Microsoft Azure Functions 2019 dataset containing 1, 332, 032 daily invocations against five baseline algorithms across three workload types. Key findings: 1. CASR eliminates wasted memory time across all evaluated workloads2. CASR reduces cold start rate by 3. 848 to 14. 929 percentage points compared to FaaSCache3. Novel K=4 experiment reduces cold start rate by up to 5. 900 percentage points over original K=3 design All code available at: https: //github. com/Krishn4nmol/CASRProject

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