Automatic I/O scheduling algorithm selection for parallel file systems

Research topic and goals

This short proposal describes an ongoing research collaboration between researchers from the Barcelona Supercomputing Center (BSC) and the Federal University of Rio Grande do Sul (UFRGS). This collaboration focuses on automatic I/O scheduling algorithm selection for parallel file systems.

During IOLanes European project (FP7-248615), Ramon Nou, from BSC, used pattern matching to automatically select the best disk scheduling algorithm (in the level of block requests to a device) depending on the access pattern observed at a given moment.

Francieli Zanon Boito, from UFRGS, focused on a similar subject during her Ph.D. thesis (concluded earlier this year). On her thesis, machine learning algorithms were used to generate decision trees based on previously obtained results to select the best scheduling algorithm to parallel file system servers (in the context of file offset requests). This decision is taken based on information about applications’ access pattern and the servers’ platform characteristics.

Due to the good results obtained with both strategies, during the last JLESC workshop (in Barcelona), Francieli and Ramon discussed this collaboration, aiming at using the pattern matching strategy to select between scheduling algorithms in the context of parallel file systems. In order to validate this idea, the technique will be implemented in the AGIOS scheduling library, developed at UFRGS, and tested with the PVFS parallel file system. Besides Francieli and Ramon, other people from their research groups will be involved in this collaboration, and joint publications are expected.

BSC, INRIA and UFRGS are involved in the H2020/EUB HPC4E project. HPC4E applications would be interesting as use cases for experimentations to validate the contributions of this JLESC collaboration.

Results for 2015/2016

We have results with an armed-bandid approach, which tries to select the I/O scheduler based on a probability-guided approach. The results are better than the original OrangeFS behaviour. This approach was used to see if AGIOS and the workloads are working with changes of the scheduler every 5 seconds (i.e.).

Results for 2016/2017

We have some new results, but the progress is slow due to the lack of personnel. Key points:

  • Armed bandit implementation, with a 4s window, show a 15% of performance improvement over OrangeFS original scheduler.
  • We analyzed MPI-IO Test benchmark with different patterns, request size, and processes.
  • We provided 120 executions to the DTW pattern matching mechanism from IOAnalyzer.
  • The precision of the match goes from 0.65 to 0.8, depending on the DTW threshold selected.
  • The executions using different orders, provide a right prediction rate over 0.75.
  • We also predict the next pattern, the results show that the prediction is 70% right.

Visits and meetings

Email interaction. Meetings in JLESC’16 at Kobe. Telco to discuss new results.

Impact and publications

None yet.

    Future plans

    As the results are promising the next steps are to put the pattern matching or similar algorithm to learn about the running workload and try to select the best expected scheduler for the next period.

    The project has very low interaction and progress during 2017, but it is not cancelled.


    1. Boito, Francieli Zanon, Rodrigo Virote Kassick, Philippe O. A. Navaux, and Yves Denneulin. 2016. “Automatic I/O Scheduling Algorithm Selection for Parallel File Systems.” Concurrency And Computation: Practice and Experience 28 (8): 2457–72. doi:10.1002/cpe.3606.
        author = {Boito, Francieli Zanon and Kassick, Rodrigo Virote and Navaux, Philippe O. A. and Denneulin, Yves},
        title = {Automatic I/O scheduling algorithm selection for parallel file systems},
        journal = {Concurrency and Computation: Practice and Experience},
        volume = {28},
        number = {8},
        issn = {1532-0634},
        url = {},
        doi = {10.1002/cpe.3606},
        pages = {2457--2472},
        keywords = {I/O scheduling, parallel file systems, high-performance computing},
        year = {2016},
        note = {cpe.3606}
    2. Nou, R., J. Giralt, and A. Cortes. 2012. “Automatic I/O Scheduler Selection through Online Workload Analysis.” In IEEE International Conference On Autonomic and Trusted Computing, 431–38. doi:10.1109/UIC-ATC.2012.12.
        author = {Nou, R. and Giralt, J. and Cortes, A.},
        booktitle = {IEEE International Conference on Autonomic and Trusted Computing},
        doi = {10.1109/UIC-ATC.2012.12},
        month = sep,
        pages = {431--438},
        title = {Automatic I/O scheduler selection through online workload analysis},
        year = {2012}