Clusters, multi-cluster systems and the grid are becoming popular high performance computing infrastructures. How jobs are scheduled on them greatly influence the performance. Since jobs are online in most computer systems, ...

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) ...

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. ...

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 ...

Performance evaluation in multi-cluster processor co-allocation - like in many other parallel job scheduling problems - is mostly done by computing the average metric value for the entire job stream. This does not give a ...

Computer processing power is increasing at a very high rate. A computer considered to be fast today may not be fast in a few years to come. At the same time, the number and complexity of resource intensive computer ...