@inproceedings{e6ba35f29ad64bfcbe36c308d50571be,
title = "WASC: Adapting scheduler configurations for heterogeneous mapreduce workloads",
abstract = "MapReduce has emerged as a popular programming paradigm for data intensive computing in both scientific and commercial applications. On a MapReduce cluster, modern resource negotiation frameworks like Hadoop YARN and Mesos support scheduling of jobs submitted by multiple tenants. However, existing job schedulers lacks the automatic adaption to workload variations in their scheduling configuration, which is crucial for the jobs{\textquoteright} latencies because it determines how to share resources among the latest jobs in the system. The major challenge here is, to a MapReduce cluster scheduler, The performance of different configurations depends not only on the number of jobs in different queues, but also on their workload characteristics, which refer to the type and size of jobs. We introduce a workload-adaptive scheduling configuration (WASC) framework for heterogeneous MapReduce jobs. WASC identifies the optimal configuration for them by reasoning about their performances under different configurations.",
keywords = "Cluster schedulers, Configurations, MapReduce, Workload heterogeneous",
author = "Siyi Wang and Fan Zhang and Rui Han",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 1st Workshop on Big Scientific Data Benchmarks, Architecture, and Systems, SDBA 2018 ; Conference date: 12-06-2018 Through 12-06-2018",
year = "2019",
doi = "10.1007/978-981-13-5910-1_4",
language = "English",
isbn = "9789811359095",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "45--54",
editor = "Rui Ren and Chen Zheng and Jianfeng Zhan",
booktitle = "Big Scientific Data Benchmarks, Architecture, and Systems - 1st Workshop, SDBA 2018, Revised Selected Papers",
address = "Germany",
}