A fuzzy neural network based scheduling algorithm for job assignment on computational grids
NBiS'07 Proceedings of the 1st international conference on Network-based information systems
Unfairness metrics for space-sharing parallel job schedulers
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
| Hi-index | 0.00 |
Coordinated scheduling across multiple sites over the grid has become a possibility due to grid technologies such as Globus and the Silver meta-scheduler. This would allow user jobs to be transparently executed at remote sites across the grid, instead of a particular local cluster. Previous research has shown this type of job distribution to be beneficial in terms of average metrics such as loss of capacity and turnaround time. This research has sparked interest in implementing such schemes, for example on the Cluster Ohio system. However, an issue that has not been addressed is that of fairness - will jobs at less loaded sites be significantly adversely affected by the distributed scheduling schemes? Trace based simulations show that indeed, there can be considerable unfairness to the less loaded sites when previously proposed distributed scheduling schemes are used. We assess approaches to enhance fairness to jobs at local sites and show that they improve fairness while also providing very good overall performance.