Wisethrottling: A new asynchronous task scheduler for mitigating i/o bottleneck in large-scale datacenter servers

Fang Lv, Lei Liu, Hui Min Cui, Lei Wang, Ying Liu, Xiao Bing Feng, Pen Chung Yew

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Datacenter servers are stepping into an era marked by powerful multi- /many-core processors. Severe problems such as I/O contentions in those large-scale platforms pose an unprecedented challenge. Prior studies primarily considered I/O bandwidth as a major performance bottleneck. However, ourwork reveals that in many cases the fundamental cause of I/O contentions is the inefficiency of OS schedulers. Particularly, the modern system is not aware of this fact and thus suffers from poor I/O performance, especially for datacenter servers. Based on our findings, we propose a new software-based scheduling approach, WiseThrottling, to reduce I/O contention. WiseThrottling performs asynchronous and self-adjustment scheduling for concurrent tasks.We evaluate our approach across a wide range of C/OpenMP/MapReduce workloads on a 64-core server in Dawning Cluster datacenter. The experimental results exhibit that WiseThrottling is effective for reducing the I/O bottleneck and it can improve the overall system performance by up to 207 %.

Original languageEnglish (US)
Article numberA013
Pages (from-to)3054-3093
Number of pages40
JournalJournal of Supercomputing
Volume71
Issue number8
DOIs
StatePublished - Aug 1 2015

Keywords

  • I/O contention
  • Multi-/many-core server
  • Resource description
  • Scheduling

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