Resource intensity aware job scheduling in a distributed cloud

A Dominant Resource Fairness (DRF) based scheme for job scheduling in distributed cloud computing systems which was modeled as multi-job scheduling and multi-resource allocation coupling problem is proposed, where the resource pool is constructed from a large number of distributed heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, storage and bandwidth. By introducing dominant resource share of jobs and virtual machines, the multi-job scheduling and multi-resource allocation joint mechanism significantly improves the cloud system's resource utilization, yet with a substantial reduction of job completion times. We show through experiments and case studies the superior performance of the algorithms in practice.