Dynamic I/O-Aware Scheduling for Batch-Mode Applications on Chip Multiprocessor Systems of Cluster Platforms

• Published in: Journal of computer science and technology Vol. 29; no. 1; pp. 21 - 37
• Main Author: 吕方 崔慧敏 王蕾 刘磊 武成岗 冯晓兵 游本中
• Format: Journal Article
• Language: English
• Published: Boston Springer US 2014; Springer Nature B.V; Institute of Information Science, Academia Sinica, Taibei 115, China; State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China%State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China%Department of Computer Science and Engineering, University of Minnesota at Twin Cities, Minneapolis, MN 55455, U.S.A
• Subjects: Algorithms; Analysis; Artificial Intelligence; Barriers; batch; Batch processes; Batch processing; chip; Cloud computing; Clusters; co-running; Computer Science; Computers; contention; Data Structures and Information Theory; Datasets; Employment; Information Systems Applications (incl.Internet); Linux; Multiprocessing; multiprocessor; Original Paper; processing; R&D; Research & development; Resource utilization; Run time (computers); Scheduling; Servers; Service centers; Software Engineering; Studies; Theory of Computation; Workload; Workloads; co-running; scheduling; chip multiprocessor; batch processing; I/O contention
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-013-1409-2

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.