Abstract-Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle distributed machine learning (ML) workloads given the following criteria: high resource utilization, fair resource allocation and low sharing overhead. To solve this problem, we propose a new CMS named Dorm, incorporating a dynamicallypartitioned cluster management mechanism and an utilizationfairness optimizer. Specifically, Dorm uses the container-based virtualization technique to partition a cluster, runs one application per partition, and can dynamically resize each partition at application runtime for resource efficiency and fairness. Each application directly launches its tasks on the assigned partition without petitioning for resources frequently, so Dorm imposes flat sharing overhead. Extensive performance evaluations showed that Dorm could simultaneously increase the resource utilization by a factor of up to 2.32, reduce the fairness loss by a factor of up to 1.52, and speed up popular distributed ML applications by a factor of up to 2.72, compared to existing approaches. Dorm's sharing overhead is less than 5% in most cases.
Recently in the literature, Dorm REF advocates partitioning an ML cluster, runs one ML application per partition, and dynamically resizes the partitions for resource efficiency and fairness, by solving a mixed integer linear program (MILP) using a standard solver.
Towards Distributed Machine Learning in Shared Clusters: A Dynamically-Partitioned Approach
2017 IEEE International Conference on Smart Computing (SMARTCOMP)
2017 IEEE International Conference on Smart Computing (SMARTCOMP)
Computer Science