SS6: Online Short-Code RAID-6 Scaling by Optimizing New Disk Location and Data Migration

Author:

Yuan Zhu12345,You Xindong3,Lv Xueqiang23,Xie Ping12345

Affiliation:

1. Computer College, Qinghai Normal University, Xining 810008, China

2. The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Xining 810008, China

3. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China

4. Key Laboratory of Internet of Things of Qinghai Province, Xining 810016, China

5. Academy of Plateau Science and Sustainability, Xining 810016, China

Abstract

Abstract Thanks to excellent reliability, availability, flexibility and scalability, redundant arrays of independent (or inexpensive) disks (RAID) are widely deployed in large-scale data centers. RAID scaling effectively relieves the storage pressure of the data center and increases both the capacity and I/O parallelism of storage systems. To regain load balancing among all disks including old and new, some data usually are migrated from old disks to new disks. Owing to unique parity layouts of erasure codes, traditional scaling approaches may incur high migration overhead on RAID-6 scaling. This paper proposes an efficient approach based Short-Code for RAID-6 scaling. The approach exhibits three salient features: first, SS6 introduces $\tau $ to determine where new disks should be inserted. Second, SS6 minimizes migration overhead by delineating migration areas. Third, SS6 reduces the XOR calculation cost by optimizing parity update. The numerical results and experiment results demonstrate that (i) SS6 reduces the amount of data migration and improves the scaling performance compared with Round-Robin and Semi-RR under offline, (ii) SS6 decreases the total scaling time against Round-Robin and Semi-RR under two real-world I/O workloads (iii) the user average response time of SS6 is better than the other two approaches during scaling and after scaling.

Funder

National Natural Science Foundation of China

Provincial Natural Science Foundation of Qinghai

Defense-related Science and Technology Key Lab Fund

Qin Xin Talents Cultivation Program of Beijing Information Science and Technology University

Research Planning of Beijing Municipal Commission of Education

Publisher

Oxford University Press (OUP)

Subject

General Computer Science

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