Abstract
The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu, an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art.
| Original language | English |
|---|---|
| Article number | 11 |
| Journal | ACM Transactions on Storage |
| Volume | 20 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 4 2024 |
Funding
This work has been sponsored in part by the National Science Foundation under grants CCF-1919113, CNS-1405697, CNS-1615411, CNS-1565314/1838271 OAC-1835890, CSR-2312785, CSR-2106634/2312785, and CCF-1919113/1919075. This research also used resources of the Oak Ridge Leadership Computing Facility, located in the National Center for Computational Sciences at the Oak Ridge National Laboratory, which is supported by the Office of Science of the DOE under Contract DE-AC05-00OR22725. We also acknowledge the support of EUPEX, which has received funding from the European High-Performance Computing Joint Undertaking (JU) under GA No 101033975. The JU receives support from the European Union's Horizon 2020 research and innovation programme, France, Germany, Italy, Greece, United Kingdom, Czech Republic, and Croatia. This work is also sponsored by the grants in BITS Pilani - BBF/BITS(G)/FY2022-23/BCPS-123, GOA/ACG/2022-2023/Oct/11, and BPGC/RIG/2021-22/06-2022/02. This work has been sponsored in part by the National Science Foundation under grants CCF-1919113, CNS-1405697, CNS-1615411, CNS-1565314/1838271 OAC-1835890, CSR-2312785, CSR-2106634/2312785, and CCF-1919113/1919075. This research also used resources of the Oak Ridge Leadership Computing Facility, located in the National Center for Computational Sciences at the Oak Ridge National Laboratory, which is supported by the Office of Science of the DOE under Contract DE-AC05-00OR22725. We also acknowledge the support of EUPEX, which has received funding from the European High-Performance Computing Joint Undertaking (JU) under GA No 101033975. The JU receives support from the European Union\u2019s Horizon 2020 research and innovation programme, France, Germany, Italy, Greece, United Kingdom, Czech Republic, and Croatia. This work is also sponsored by the grants in BITS Pilani - BBF/BITS(G)/FY2022-23/BCPS-123, GOA/ACG/2022-2023/Oct/11, and BPGC/RIG/2021-22/06-2022/02.
Keywords
- Parallel file system
- lustre
- progressive file layout
- time series modeling