Workload-Adaptive Configuration Tuning for Hierarchical Cloud Schedulers

Workload-Adaptive Configuration Tuning for Hierarchical Cloud Schedulers

Han, Rui (Beijing Institute of Technology)
Liu, Chi Harold (Beijing Institute of Technology)
Zong, Zan (Tsinghua University)
Chen, Lydia Y. (TU Delft Data-Intensive Systems)
Liu, Wending (Beijing Institute of Technology)
Wang, Siyi (Institute of Computing Technology Chinese Academy of Sciences)
Zhan, Jianfeng (Institute of Computing Technology Chinese Academy of Sciences)

2019-12-01

Cluster schedulers provide flexible resource sharing mechanism for best-effort cloud jobs, which occupy a majority in modern datacenters. Properly tuning a scheduler's configurations is the key to these jobs' performance because it decides how to allocate resources among them. Today's cloud scheduling systems usually rely on cluster operators to set the configuration and thus overlook the potential performance improvement through optimally configuring the scheduler according to the heterogeneous and dynamic cloud workloads. In this paper, we introduce AdaptiveConfig, a run-time configurator for cluster schedulers that automatically adapts to the changing workload and resource status in two steps. First, a comparison approach estimates jobs' performances under different configurations and diverse scheduling scenarios. The key idea here is to transform a scheduler's resource allocation mechanism and their variable influence factors (configurations, scheduling constraints, available resources, and workload status) into business rules and facts in a rule engine, thereby reasoning about these correlated factors in job performance comparison. Second, a workload-adaptive optimizer transforms the cluster-level searching of huge configuration space into an equivalent dynamic programming problem that can be efficiently solved at scale. We implement AdaptiveConfig on the popular YARN Capacity and Fair schedulers and demonstrate its effectiveness using real-world Facebook and Google workloads, i.e., successfully finding best configurations for most of scheduling scenarios and considerably reducing latencies by a factor of two with low optimization time.

Cloud datacenter
cluster scheduler
configuration
job latency
YARN

http://resolver.tudelft.nl/uuid:a07f9851-1949-43f8-ae79-9b4518a3f367

https://doi.org/10.1109/TPDS.2019.2923197

1045-9219

IEEE Transactions on Parallel and Distributed Systems, 30 (12), 2879-2895

Institutional Repository

journal article

© 2019 Rui Han, Chi Harold Liu, Zan Zong, Lydia Y. Chen, Wending Liu, Siyi Wang, Jianfeng Zhan