ASF: An Adaptive Scaling Framework for High Scalability of XOR-Based RAID Systems

Sponsored by NSF CNS-CSR

Abstract:

Explosive growth in data volume, heterogeneity, and complexity imposes unprecedented challenges in data analysis and organization in data centers. RAID particularly XOR-based RAID plays an important role to provide both reliability and high performance storage services for these data centers. However, they suffer from problems on the scalability issue due to multi-folded factors, including: heterogeneous RAID layouts and various erasure codes, high overhead of existing scaling process to significantly downgrade the storage performance, and lack of bidirectional scaling support.

The objective of this project is to address the scalability challenge for storage systems in large data centers. This project designs novel techniques to exploit XOR-based parity codes to achieve efficient scaling, develops a series of scalable XOR-based erasure codes to bridge the relations among heterogeneous RAID forms for interoperability, and integrates various erasure codes in a framework to provide a unified user interface for RAID scaling.

Personnel

- Principal Investigator

- Collaborators

- Post Doctoral Researcher

- Graduate Students

Recent Publications

  1. Y. Guo, P. Huang, B. Young, T. Lu, X. He, and Q. Liu, “Alleviating DRAM Refresh Overhead via Inter-rank Piggyback Caching”, The 23rd IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), October 5-7, 2015.
  2. C. Du, C. Wu, J. Li, M. Guo, and X. He, “BPS: A Balanced Partial Stripe Write Scheme to Improve the Performance of RAID-6”, Proc. Of the IEEE Cluster, Chicago, Illinois, USA, Sept. 8-11, 2015 (acceptance rate: 38/157=24%).
  3. C. Wu, X. He, J. Li, and M. Guo, “Code 5-6: An Efficient MDS Array Coding Scheme to Accelerate Online RAID Level Migration”, Proc of the International Conference on Parallel Processing (ICPP), 2015, Beijing, China (acceptance rate: 99/325=32.5%).
  4. S. Li, Q. Cao, S. Wan, W. Zhang, C. Xie, X. He, S. Pradeep, “PPM: A Partitioned and Parallel Matrix Algorithm to Accelerate Encoding/Decoding Process of Asymmetric Parity Erasure Codes”, Proc of the International Conference on Parallel Processing (ICPP), 2015, Beijing, China (acceptance rate: 99/325=32.5%).
  5. P. Subedi, P. Huang, B. Young, and X. He, “FINGER: A Novel Erasure Coding Scheme Using Fine Granularity Blocks to Improve Hadoop Write and Update Performance,” Proc. Of the 10th IEEE International Conference on Networking, Architecture, and Storage (NAS), August 6-7, 2015, Boston, MA (acceptance rate: 30/94=32%).
  6. Y. Yu, W. Xiao, X. He, H. Guo, and Y. Wang, “A Stall-Aware Warp Scheduling for Dynamically Optimizing Thread-level Parallelism in GPGPUs”, the 29th International Conference on Supercomputing (ICS), June 8-11, Newport Beach, CA (acceptance rate: 40/160=25%).
  7. Y. Zhou, F. Wu, P. Huang, X. He, C. Xie, and J. Zhou, “An Efficient Page-level FTL to Optimize Address Translation in Flash Memory”, The European Conference on Computer Systems (EuroSys), April 2015 (acceptance rate: 32/150=21%).
  8. P. Subedi, P. Huang, X. He, M. Zhang, and J. Han, "A Hybrid Erasure-Coded ECC Scheme to Improve Performance and Reliability of Solid State Drives", 33rd IEEE International Performance Computing and Communications Conference, Austin, TX, December 2014.
  9. S. Li and X. He and S. Wan and Y. Guo and P. Huang and D. Chen and Q. Cao and C. Xie (2014). Exploiting Decoding Computational Locality to Improve the I/O Performance of an XOR-coded Storage Cluster under Concurrent Failures. International Symposium on Reliable Distributed Systems (SRDS). Nara, Japan.

Sponsors