Journal
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Volume 30, Issue 12, Pages 2879-2895Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2019.2923197
Keywords
Resource management; Scheduling; Google; Yarn; Tuning; Facebook; Task analysis; Cloud datacenter; cluster scheduler; configuration; job latency; YARN
Funding
- National Key Research and Development Plan of China [2018YFB1003701, 2018YFB1003700]
- National Natural Science Foundation of China [61872337]
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Cluster schedulers provide flexible resource sharing mechanism for best-effort cloud jobs, which occupy a majority in modern datacenters. Properly tuning a scheduler & x0027;s configurations is the key to these jobs' performance because it decides how to allocate resources among them. Today & x0027;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 & x0027;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.
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