Fragment Aware Scheduling for Advance Reservations in Multiprocessor Systems

Abstract

In multiprocessor environment, resource reservation technology will split the continuous idle resources and generate resource fragments which would reduce resource utilization and job acceptance rate. In this paper, we defined resource fragments produced by resource reservation and proposed scheduling algorithms based on fragment-aware, the designs of which focus on improve acceptance ability of following-up jobs. Based on resource fragment-aware, we proposed two algorithms, Occupation Rate Best Fit and Occupation Rate Worst Fit, and in combination with heuristic algorithms, PE Worst Fit - Occupation Rate Best Fit and PE Worst Fit - Occupation Rate Worst Fit are put forward. We not only realized and analyzed algorithms in simulation, but also studied relationship between task properties and algorithms' performance. Experiments proved that PE Worst Fit - Occupation Worst Fit provides the best job acceptance rate and Occupation Rate Worst Fit has the best performance on average slowdown.