Erasure Coding-Oriented Data Update for Cloud Storage: A Survey

Abstract

Erasure coding is the leading technique to achieve resilient redundancy in cloud storage systems. However, it introduces two prominent issues: data repair and data update. Compare to data repair, data update is much more common. A variety of update schemes based on erasure coding have been proposed in the literature to optimize data update, such as computation optimization, network traffic overhead reduction, IO overhead reduction, and modern hardware acceleration. However, all of these techniques were proposed individually previously. In this work, we seek to summarize them systematically and group them in a new form. First, we generalize the state-of-the-art researches and introduce existing classifications. Moreover, based on our observation, we propose two classifications: resource-based classification and tier-based classification. In resource-based classification, we group these techniques according to the resource they optimize and introduce them in detail. In tier-based classification, we propose a novel hybrid technique framework with five tiers and conduct a comprehensive comparison between these techniques. We make a conjecture that most techniques in different tiers can be used jointly. Finally, we conclude the research challenges and potential future works.