Efficient in-place update with grouped and pipelined data transmission in erasure-coded storage systems

- We propose a three-layer framework to support both single and multiple updates.
- We propose a workload-aware group technique to dynamically adjust the group size.
- We propose a distributed pipeline technique to distribute the computation.
- We propose a hybrid update technique to be compatible with the node failure.
- We conduct extensive experiments to confirm the advantages of our approach.

Distributed storage systems usually adopt erasure coding to achieve better tradeoff between the space efficiency and the data reliability. In-place updates are often used to overwrite the existing data rather than append the new data so as to ensure the data access efficiency. However, existing in-place update approaches either introduce significant I/O overhead or cause low update efficiency in erasure-coded storage systems due to the consistent update of parity blocks. In this paper, we propose a grouped and pipelined update scheme based on erasure codes, called Group-U, which comprises four key design features. (1) It groups the data nodes to complete the data transmission and dynamically adjusts the group size according to the update workload. (2) It pipelines the data transmission and distributes the update computation to all the participating nodes to improve the update efficiency. (3) It adopts the in-time update for data nodes and lazy-update for parity nodes to further reduce the update overhead. (4) It adjusts the occasion triggering the update to be compatible with the node failure. We design and implement Group-U on our Raid Distributed Storage System (RDFS) and conduct testbed experiments on different update schemes under various parameter settings. The analysis and experimental results show that Group-U consumes 22% increase of update overhead compared with PUM and achieves 46% reduction of update overhead compared with PDP-P and PUS. Furthermore, Group-U achieves 69%, 34% and 21% reduction of update time on average compared with PUM, PDP-P and PUS respectively.