The performance of cluster computing depends on how concurrent jobs share
multiple data center resource types like CPU, RAM and disk storage. Recent
research has discussed efficiency and fairness requirements and identified a
number of desirable scheduling objectives including so-called dominant resource
fairness (DRF). We argue here that proportional fairness (PF), long recognized
as a desirable objective in sharing network bandwidth between ongoing flows, is
preferable to DRF. The superiority of PF is manifest under the realistic
modelling assumption that the population of jobs in progress is a stochastic
process. In random traffic the strategy-proof property of DRF proves
unimportant while PF is shown by analysis and simulation to offer a
significantly better efficiency-fairness tradeoff.Comment: Submitted to Performance 201
