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

TL;DR

This paper compares FIFO and Round-Robin OS scheduling on Ubuntu for compute-intensive ML workloads and data pipelines to assess performance and energy implications. It employs real-time ML tasks (CNN and LSTM) and Spark-based pipelines, together with PowerTop measurements, to characterize CPU wakeups, utilization, and energy use under each policy. The authors discuss traditional scheduling paradigms and position FIFO and RR within a broader context of energy-aware and deadline-based scheduling. The results provide guidance for designing OS schedulers that balance throughput and energy efficiency in modern compute clusters.

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.

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