Abstract
With the advent of new computing paradigms, parallel file systems serve not only traditional scientific computing applications but also non-scientific computing applications, such as financial computing, business, and public administration. Parallel file systems provide storage services for multiple applications. As a result, various requirements need to be met. However, parallel file systems usually provide a unified storage solution, which cannot meet specific application needs. In this paper, an extended file handle scheme is proposed to deal with this problem. The original file handle is extended to record I/O optimization information, which allows file systems to specify optimizations for a file or directory based on workload characteristics. Therefore, fine-grained management of I/O optimizations can be achieved. On the basis of the extended file handle scheme, data prefetching and small file optimization mechanisms are proposed for parallel file systems. The experimental results show that the proposed approach improves the aggregate throughput of the overall system by up to 189.75%.
Similar content being viewed by others
References
Carns P H, Ligon W B, Ross R B, Thakur R. PVFS: a parallel file system for Linux clusters. In: Proceedings of the 4th Annual Linux Showcase and Conference. 2000, 317–327
Schmuck F, Haskin R. GPFS: a shared-disk file system for large computing clusters. In: Proceedings of the 10th USENIX Conference on File and Storage Technologies. 2002, 231–244
Wei B, Xiao L, Zhou B, Qin G, Yan B, Huo Z. I/O optimizations based on workload characteristics for parallel file systems. In: Proceedings of the 16th Annual IFIP International Conference on Network and Parallel Computing. 2019, 305–310
Isaila F, Balaprakash P, Wild S M, Kimpe D, Latham R, Ross R, Hovland P. Collective I/O tuning using analytical and machine learning models. In: Proceedings of the IEEE International Conference on Cluster Computing. 2015, 128–137
Byna S, Chen Y, Sun X H, Thakur R, Gropp W. Parallel I/O prefetching using MPI file caching and I/O signatures. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing. 2008, 1–12
Chen J, Liu J, Roth P, Chen Y. Using working set reorganization to manage storage systems with hard and solid state disks. In: Proceedings of the 43rd International Conference on Parallel Processing Workshops. 2014, 283–291
Costa L B, Ripeanu M. Towards automating the configuration of a distributed storage system. In: Proceedings of the 11th IEEE/ACM International Conference on Grid Computing. 2010, 201–208
Narayan S, Chandy J. Attest: attributes-based extendable storage. Journal of Systems and Software, 2010, 83(4): 548–556
Madhyastha T M, Reed D A. Learning to classify parallel input/output access patterns. IEEE Transactions on Parallel and Distributed Systems, 2002, 13(8): 802–813
Wang Y, Kaeli D. Profile-guided I/O partitioning. In: Proceedings of the 17th Annual International Conference on Supercomputing. 2003, 252–260
Habermann P, Chi C C, Alvarez-Mesa M, Juurlink B. Application-specific cache and prefetching for HEVC CABAC decoding. IEEE MultiMedia, 2017, 24(1): 72–85
Chen J, Roth P C, Chen Y. Using pattern-models to guide SSD deployment for big data applications in HPC systems. In: Proceedings of IEEE International Conference on Big Data. 2013, 332–337
He J, Bent J, Torres A, Grider G, Gibson G, Maltzahn C, Sun X H. I/O acceleration with pattern detection. In: Proceedings of the 22nd International Symposium on High-Performance Parallel and Distributed Computing. 2013, 25–36
Patrick C M, Kandemir M, Karakoy M, Son S W, Choudhary A. Cashing in on hints for better prefetching and caching in PVFS and MPI-IO. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. 2010, 191–202
Battle L, Chang R, Stonebraker M. Dynamic prefetching of data tiles for interactive visualization. In: Proceedings of the 2016 International Conference on Management of Data. 2016, 1363–1375
Al-Kiswany S, Gharaibeh A, Ripeanu M. The case for a versatile storage system. ACM SIGOPS Operating Systems Review, 2010, 44(1): 10–14
Calderon A, Garcia-Carballeira F, Sanchez L M, Garcia J D, Fernandez J. Fault tolerant file models for parallel file systems: introducing distribution patterns for every file. The Journal of Supercomputing, 2009, 47(3): 312–334
Qiu M, Sha E H M. Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems. ACM Transactions on Design Automation of Electronic Systems, 2009, 14(2): 1–30
Vilayannur M, Nath P, Sivasubramaniam A. Providing tunable consistency for a parallel file store. In: Proceedings of the 4th USENIX Conference on File and Storage Technologies. 2005, 17–30
Xue J, Yan F, Birke R, Chen L Y, Scherer T, Smirni E. PRACTISE: robust prediction of data center time series. In: Proceedings of the 11th International Conference on Network and Service Management. 2015, 126–134
Dai D, Bao F S, Zhou J, Chen Y. Block2vec: a deep learning strategy on mining block correlations in storage systems. In: Proceedings of the 45th International Conference on Parallel Processing Workshops. 2016, 230–239
Guo C, Li Y, Liu H, Wu Z. An application-oriented cache allocation and prefetching method for long-running applications in distributed storage systems. Chinese Journal of Electronics, 2019, 28(4): 773–780
Zhang S L, Catanese H, Wang A A I. The composite-file file system: decoupling the one-to-one mapping of files and metadata for better performance. In: Proceedings of the 14th USENIX Conference on File and Storage Technologies. 2016, 15–22
Hou B, Chen F. Pacaca: mining object correlations and parallelism for enhancing user experience with cloud storage. In: Proceedings of the 26th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems. 2018, 293–305
Sheoran S, Sethia D, Saran H. Optimized mapfile based storage of small files in hadoop. In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2017, 906–912
Mehmood A, Usman M, Mehmood W, Khaliq Y. Performance efficiency in hadoop for storing and accessing small files. In: Proceedings of the 7th International Conference on Innovative Computing Technology. 2017, 211–216
Carns P, Lang S, Ross R, Vilayannur M, Kunkel J, Ludwig T. Small-file access in parallel file systems. In: Proceedings of the IEEE International Symposium on Parallel & Distributed Processing. 2009, 1–11
Kuhn M, Kunkel J M, Ludwig T. Dynamic file system semantics to enable metadata optimizations in PVFS. Concurrency and Computation: Practice and Experience, 2009, 21(14): 1775–1788
Wei B, Xiao L M, Wei W, Song Y, Zhou B Y. A new adaptive coding selection method for distributed storage systems. IEEE Access, 2018, 6(1): 13350–13357
Li Z P, Yu H, Liu Y C, Liu F Q. An improved adaptive exponential smoothing model for short-term travel time forecasting of urban arterial street. Acta Automatica Sinica, 2008, 34(11): 1404–1409
Weil S A, Brandt S A, Miller E L, Long D D E. Ceph: a scalable, high-performance distributed file system. In: Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation. 2006, 307–320
Shvachko K, Kuang H, Radia S, Chansler R. The hadoop distributed file system. In: Proceedings of the 26th IEEE Symposium on Mass Storage Systems and Technologies. 2010, 1–10
Ghemawat S, Gobioff H, Leung S T. The Google file system. In: Proceedings of the 19th ACM Symposium on Operating Systems Principles. 2003, 29–43
Acknowledgements
This work was supported by the National key R&D Program of China (2018YFB0203901), the National Natural Science Foundation of China (Grant No. 61772053), the Science Challenge Project, No. TZ2016002, and the fund of the State Key Laboratory of Software Development Environment (SKLSDE-2017ZX-10).
Author information
Authors and Affiliations
Corresponding author
Additional information
An earlier version of this paper entitled “I/O Optimizations Based on Workload Characteristics for Parallel File Systems” was presented at the International Conference on Network and Parallel Computing (NPC 2019)
Bing Wei received the BS in electrical engineering and MS degrees in computer science from Capital Normal University, China in 2012 and 2015, respectively, He is currently pursuing a PhD degree in computer science at Beihang University, China. His main research interests include distributed file systems, high performance computing, software engineering, and clusters.
Limin Xiao received the BS in computer science from Tsinghua University, China in 1993, the MS and PhD degree in computer science from Institute of Computing, Chinese Academy of Sciences, China in 1996 and 1998, respectively. He is a professor of the School of Computer Science and Engineering, Beihang University, China. He is a senior membership of China Computer Federation. His main research areas are computer architecture, computer system software, high performance computing, virtualization and cloud computing.
Bingyu Zhou received the Bachelor of computer science and technology from BeiJing Wuzi University, China in 2015. She received the MS degree in computer science in BeiHang University, China in 2019. Her main research interests include bandwidth allocation and optimization of file system.
Guangjun Qin received the MS degree in computer application technology in Zhengzhou University, China in 2006 and the PhD degree in computer architecture from Beihang University, China in 2015. From 2015 to 2017, he was a Postdoctoral Fellow at Beihang University, China. Since 2017, he has been a Lecturer of College Smart City, Beijing Union University, China. His main research areas are computer architecture, storage system, information security and big data analytics.
Baicheng Yan received his BS degree in computer science and technology from Harbin Engineering University, China in 2016. He is currently pursuing a PhD degree in computer science at Beihang University, China. His research interests include high performance computing, parallel and distributed computing.
Zhisheng Huo is a Post-doctoral fellow of School of Computer Science and Engineering, Beihang University, China. His research interests include high performance computing, big data storage, bandwidth allocation and optimization of file system, and distributed storage system.
Electronic Supplementary Material
Rights and permissions
About this article
Cite this article
Wei, B., Xiao, L., Zhou, B. et al. Fine-grained management of I/O optimizations based on workload characteristics. Front. Comput. Sci. 15, 153102 (2021). https://doi.org/10.1007/s11704-020-9344-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11704-020-9344-1