Now showing items 1-6 of 6
Characteristics of fairness metrics and their effect on perceived scheduler effectiveness
Parallel job schedulers are mostly evaluated using performance metrics. Deductions however can be misleading due to selective job starvation (unfairness). To choose a better scheduler, therefore, there is a need to compare ...
Performance, fairness and effectiveness in space slicing multi-cluster schedulers
(ACTA Press, 2007)
Parallel job schedulers have mostly been evaluated/compared using performance metrics. The deductions, however, can be misleading due to selective starvation. This calls for studies in scheduler fairness. Most studies have ...
Co-allocation with communication considerations in multi-cluster systems
Processor co-allocation can be of performance benefit. This is because breaking jobs into components reduces overall cluster fragmentation. However, the slower inter-cluster communication links increase job execution times. ...
The greedy multi-cluster scheduler: performance bounds and parametric sensitivity
Most schedulers in parallel job scheduling do not put (job) schedulability into consideration when prioritizing jobs. Performance evaluation is mostly done using average values of the measurement metric. Using the average ...
A Metric of fairness for parallel job schedulers
(Wiley Interscience, 2009-08-25)
Fairness is an important aspect in queuing systems. Several fairness measures have been proposed in queuing systems in general and parallel job scheduling in particular. Generally, a scheduler is considered unfair if some ...
The Influence of Job Physical Characteristics on their Schedulability in Multi-cluster Systems
(Fountain Publishers, 2006)
Performance (and sensitivity) studies in parallel job scheduling mostly use average values of the measurement metrics over the entire job stream. This does not give an idea of relative job performance (hence starvation) ...