Assessing FIFO and Round Robin Scheduling:Effects on Data Pipeline Performance and Energy Usage

TL;DR

This paper compares FIFO and Round Robin scheduling policies in Ubuntu systems to determine their impact on performance and energy efficiency in compute-intensive machine learning data pipelines.

Contribution

It provides a comparative analysis of FIFO and RR scheduling policies specifically for real-time machine learning workloads on Ubuntu, highlighting their effects on performance and energy consumption.

Findings

FIFO and RR have different impacts on energy use and performance.

Shared vs. exclusive CPU usage patterns influence efficiency.

Results guide better scheduler choices for modern workloads.

Abstract

In the case of compute-intensive machine learning, efficient operating system scheduling is crucial for performance and energy efficiency. This paper conducts a comparative study over FIFO(First-In-First-Out) and RR(Round-Robin) scheduling policies with the application of real-time machine learning training processes and data pipelines on Ubuntu-based systems. Knowing a few patterns of CPU usage and energy consumption, we identify which policy (the exclusive or the shared) provides higher performance and/or lower energy consumption for typical modern workloads. Results of this study would help in providing better operating system schedulers for modern systems like Ubuntu, working to improve performance and reducing energy consumption in compute intensive workloads.