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 tierbased 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. INDEX TERMS data update, cloud storage, erasure coding, survey

Evaluating Erasure Codes in Dicoogle PACS

“…The paradigm introduced by the Dicoogle project empowers queries over a set of distributed repositories, which are logically indexed as a single agile unit. In [51], the authors described a cloud-based relay service [52] that acts as a communication bridge between different institutions, allowing the community to access, share and discover image records [51]. Dicoogle was proposed in [53] as an architecture for cloud [52]-based PACS archives that provides data privacy, integrity and availability.…”

“…In [51], the authors described a cloud-based relay service [52] that acts as a communication bridge between different institutions, allowing the community to access, share and discover image records [51]. Dicoogle was proposed in [53] as an architecture for cloud [52]-based PACS archives that provides data privacy, integrity and availability. The solution proposed in [53] is independent of the cloud [52] provider and core modules.…”

“…Dicoogle was proposed in [53] as an architecture for cloud [52]-based PACS archives that provides data privacy, integrity and availability. The solution proposed in [53] is independent of the cloud [52] provider and core modules. Operational metrics for various medical image modalities were tabulated and compared for Google Storage, Amazon S3 and LAN PACS [53].…”

“…In [58], the software architecture of Dicoogle based on different plugins is presented, which includes a storage plugin with two approaches, one aimed at local storage and the other at storage in the cloud [52]. Additionally, the Dicoogle software architecture is supported by a document-based indexing system and peer-to-peer (P2P) protocols.…”

Data Recovery Approach with Optimized Cauchy Coding in Distributed Storage System

“…To improve remote sensing retrieval mechanisms, Li & Zhang [12] propose widely open datasets with evaluation metrics. Xiao & Zhou [13] propose classifiers based on resource optimization and framework to minimize data repair and update concerns in erasure coding schemes. Gong & Sung [14] introduce zig-zag codes as erasure codes for data decoding.…”