Dynamic I/O-aware scheduling for batch-mode applications on chip multiprocessor systems of cluster platforms

Fang Lv, Hui Min Cui, Lei Wang, Lei Liu, Cheng Gang Wu, Xiao Bing Feng, Pen Chung Yew

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Efficiency of batch processing is becoming increasingly important for many modern commercial service centers, e.g., clusters and cloud computing datacenters. However, periodical resource contentions have become the major performance obstacles for concurrently running applications on mainstream CMP servers. I/O contention is such a kind of obstacle, which may impede both the co-running performance of batch jobs and the system throughput seriously. In this paper, a dynamic I/O-aware scheduling algorithm is proposed to lower the impacts of I/O contention and to enhance the co-running performance in batch processing. We set up our environment on an 8-socket, 64-core server in Dawning Linux Cluster. Fifteen workloads ranging from 8 jobs to 256 jobs are evaluated. Our experimental results show significant improvements on the throughputs of the workloads, which range from 7% to 431%. Meanwhile, noticeable improvements on the slowdown of workloads and the average runtime for each job can be achieved. These results show that a well-tuned dynamic I/O-aware scheduler is beneficial for batch-mode services. It can also enhance the resource utilization via throughput improvement on modern service platforms.

Original languageEnglish (US)
Pages (from-to)21-37
Number of pages17
JournalJournal of Computer Science and Technology
Issue number1
StatePublished - Jan 2014

Bibliographical note

Funding Information:
Regular Paper Supported by the National High Technology Research and Development 863 Program of China under Grant No. 2012AA010902, the National Basic Research 973 Program of China under Grant No. 2011CB302504, and the National Natural Science Foundation of China under Grant Nos. 61202055, 60925009, 60921002, 61100011. ①Migration scenario: Migrating batch processes to the aws cloud. http://d36cz9buwru1tt.cloudfront.net/CloudMigration-scenario-batch-apps.pdf, August 2013 ②Microsoft. Batch applications — The hidden asset, August 2013. ③Getting started with batch processing. http://support.hmdc.harvard.edu/book/export/html/402, August 2013. ©2014 Springer Science + Business Media, LLC & Science Press, China


  • I/O contention
  • batch processing
  • chip multiprocessor
  • co-running
  • scheduling


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