POS: A Popularity-based Online Scaling scheme for RAID-structured storage systems

The ever-increasing demand of storage capability leads to scaling requirement in RAID-structured storage systems. Previous approaches to RAID scaling mainly focus on minimizing data migration, without considering the user-level application accesses. However, the mixed scaling I/Os and user accesses in practical systems will interfere with each other, which results in significant performance degradation of both the data migration time and the user response time. In this paper, we divide the whole storage space into multiple zones and measure the popularity (mainly using the metric of access frequency) of each zone. Based on the measured popularity, we propose an online scheme, namely Popularity-based Online Scaling (POS), to scale RAID-structured storage systems. The main idea of POS is to scale storage areas with high popularity first so as to better exploit workload locality. POS can efficiently alleviate the performance degradation of user response time and data migration time during the scaling process. It can be readily deployed atop various conventional RAID scaling approaches to improve their performance. To evaluate the performance of POS, we implement FastScale and FastScale with POS (POS-FS) in the same system. Through extensive benchmark studies on real-system workloads, we show that POS can efficiently reduce the response time to user requests and scaling I/Os and improve the sequentiality of data accesses.

[1]  Chentao Wu,et al.  GSR: A Global Stripe-Based Redistribution Approach to Accelerate RAID-5 Scaling , 2012, 2012 41st International Conference on Parallel Processing.

[2]  Chentao Wu,et al.  SDM: A Stripe-Based Data Migration Scheme to Improve the Scalability of RAID-6 , 2012, 2012 IEEE International Conference on Cluster Computing.

[3]  Weimin Zheng,et al.  FastScale: Accelerate RAID Scaling by Minimizing Data Migration , 2011, FAST.

[4]  Toni Cortes,et al.  CRAID: online RAID upgrades using dynamic hot data reorganization , 2014, FAST.

[5]  Jiwu Shu,et al.  SLAS: An efficient approach to scaling round-robin striped volumes , 2007, TOS.

[6]  Ludmila Cherkasova,et al.  Analysis of enterprise media server workloads: access patterns, locality, content evolution, and rates of change , 2004, IEEE/ACM Transactions on Networking.

[7]  Antony I. T. Rowstron,et al.  Write off-loading: Practical power management for enterprise storage , 2008, TOS.

[8]  Radu Marculescu,et al.  QuaLe: A Quantum-Leap Inspired Model for Non-stationary Analysis of NoC Traffic in Chip Multi-processors , 2010, 2010 Fourth ACM/IEEE International Symposium on Networks-on-Chip.

[9]  Gregory R. Ganger,et al.  The DiskSim Simulation Environment Version 4.0 Reference Manual (CMU-PDL-08-101) , 1998 .

[10]  Keqin Li,et al.  Rethinking RAID-5 Data Layout for Better Scalability , 2014, IEEE Transactions on Computers.

[11]  David A. Patterson,et al.  A Simple Way to Estimate the Cost of Downtime , 2002, LISA.