Revitalising the Single Batch Environment: A 'Quest' to Achieve Fairness and Efficiency
Supriya Manna, Krishna Siva Prasad Mudigonda
TL;DR
This work tackles fair and efficient CPU scheduling in a single-batch, uni-processing setting by introducing FairBatch, a preemptive scheduler that uses a defined fairness ratio to rank and select jobs and a dynamic time quantum to balance turnaround, waiting, and responsiveness. The core idea centers on the metric $fairnessRatio = (bursttime - remainingtime + waitingtime) / (bursttime * preemptioncount)$, guiding periodic reordering to favor progress while controlling preemption. The authors provide theoretical analyses of the ratio, propose a practical algorithm with Select/Runner to minimize overhead, and validate performance across diverse bursttime distributions with a large public benchmark dataset. Overall, FairBatch demonstrates a strong efficiency–fairness trade-off, offering stability, reduced starvation, and competitive metrics compared with traditional policies in batch environments.
Abstract
In the realm of computer systems, efficient utilisation of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimise process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. This research proposes a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilising periodic sorting technique to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimised solution for batch scheduling while ensuring system responsiveness.