  High-performance computing (HPC) is undergoing significant changes. Next
generation HPC systems are equipped with diverse global and local resources,
such as I/O burst buffer resources, memory resources (e.g., on-chip and
off-chip RAM, external RAM/NVRA), network resources, and possibly other
resources. Job schedulers play a crucial role in efficient use of resources.
However, traditional job schedulers are single-objective and fail to efficient
use of other resources. In this paper, we propose ROME, a novel
multi-dimensional job scheduling framework to explore potential tradeoffs among
multiple resources and provides balanced scheduling decision. Our design
leverages genetic algorithm as the multi-dimensional optimization engine to
generate fast scheduling decision and to support effective resource
utilization.
