Towards Multi-Resource Fair Allocation with Placement Constraints

Wei Wang, Baochun Li, Ben Liang, Jun Li
2016 Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science - SIGMETRICS '16  
Multi-resource fair schedulers have been widely implemented in compute clusters to provide service isolation guarantees. Existing multi-resource sharing policies, notably dominant resource fairness (DRF) and its variants, are designed for unconstrained jobs that can run on all machines in a cluster. However, an increasing number of datacenter jobs specify placement constraints and can only run on a particular class of machines meeting specific hardware/software requirements (e.g., GPUs or a
more » ... icular kernel version). We show that directly extending existing policies to constrained jobs either compromises isolation guarantees or allows users to gain more resources by deceiving the scheduler. It remains unclear how multi-resource fair sharing is defined and achieved in the presence of placement constraints. We address this open problem by a new sharing policy, called task share fairness (TSF), that provides provable isolation guarantees and is strategy-proof against gaming the allocation policy. TSF is shown to be envy-free and Pareto optimal as well.
doi:10.1145/2896377.2901493 dblp:conf/sigmetrics/WangLLL16 fatcat:n3kboaagbnea5d24pl34atlfte