Resource Availability-Aware Advance Reservation for Parallel Jobs with Deadlines

TL;DR

This paper introduces a slot-based data structure and scheduling policies to efficiently allocate resources for parallel advance reservation jobs with deadlines in multiprocessor systems, improving acceptance rates and reducing job slowdown.

Contribution

It proposes a novel resource organization data structure and suite of scheduling policies tailored for parallel AR jobs with deadlines, validated through simulation.

Findings

PE-Worst-Fit algorithm achieves highest acceptance rate

First-Fit algorithm results in lowest average slowdown

Scheduling performance depends on job size, load, and flexibility

Abstract

Advance reservation is important to guarantee the quality of services of jobs by allowing exclusive access to resources over a defined time interval on resources. It is a challenge for the scheduler to organize available resources efficiently and to allocate them for parallel AR jobs with deadline constraint appropriately. This paper provides a slot-based data structure to organize available resources of multiprocessor systems in a way that enables efficient search and update operations, and formulates a suite of scheduling policies to allocate resources for dynamically arriving AR requests. The performance of the scheduling algorithms were investigated by simulations with different job sizes and durations, system loads and scheduling flexibilities. Simulation results show that job sizes and durations, system load and the flexibility of scheduling will impact the performance metrics of all the scheduling algorithms, and the PE-Worst-Fit algorithm becomes the best algorithm for the scheduler with the highest acceptance rate of AR requests, and the jobs with the First-Fit algorithm experience the lowest average slowdown. The data structure and scheduling policies can be used to organize and allocate resources for parallel AR jobs with deadline constraint in large-scale computing systems.