New YARN non-exclusive resource management scheme through opportunistic idle resource assignment

Zhengyu Yang, Yi Yao, Han Gao, Jiayin Wang, Ningfang Mi, Bo Sheng

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Efficiently managing resources and improving throughput in a large-scale cluster has become a crucial problem with the explosion of data processing applications in recent years. Hadoop YARN and Mesos, as two universal resource management platforms, have been widely adopted in the commodity cluster for co-deploying multiple data processing frameworks, such as Hadoop MapReduce and Apache Spark. However, in the existing resource management, a certain amount of resources are exclusively allocated to a running task and can only be re-assigned after that task is completed. This exclusive mode unfortunately leads to a potential problem that may under-utilize the cluster resources and degrade system performance. To address this issue, we propose a novel opportunistic and efficient resource allocation scheme, named OpERA, which breaks the barriers among the encapsulated resource containers by leveraging the knowledge of actual runtime resource utilizations to re-assign opportunistic available resources to the pending tasks. OpERA avoids incurring severe performance interference to active tasks by further using two approaches to efficiently balances the starvations of reserved tasks and normal queued tasks. We implement and evaluate OpERA in Hadoop YARN v2.5. Our experimental results show that OpERA significantly reduces the average job execution time and increases the resource (CPU and memory) utilizations.

Original languageEnglish
Article number8450035
Pages (from-to)696-709
Number of pages14
JournalIEEE Transactions on Cloud Computing
Issue number2
StatePublished - 1 Apr 2021


  • Hadoop YARN
  • MapReduce scheduling
  • Resource allocation
  • opportunistic
  • reservation
  • spark
  • starvation


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