On the Sub-Packetization Size and the Repair Bandwidth of Reed-Solomon Codes

Li, Weiqi; Wang, Zhiying; Jafarkhani, Hamid

doi:10.1109/tit.2019.2917425

Cited by 18 publications

(19 citation statements)

References 36 publications

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“…MSR vector codes are well studied in [10], [38]- [44], where each symbol is a vector. As one of the most popular codes in practical systems, Reed-Solomon (RS) code and its repair is studied in [11], [45]- [48], where each symbol is a scalar.…”

Section: Flexible Msr Codesmentioning

confidence: 99%

“…Let B be the base field of F such that F = B L . For repairing RS codes, [45] and [48] shows that any linear repair scheme for a given RS(n, k) over the finite field F = B L is equivalent to finding a set of repair polynomials p * ,v (x) such that for the failed node…”

Section: Conditionmentioning

confidence: 99%

“…We take the construction in [48] as the RS(n + k j − k a , k j ), j ∈ [a] codes in Construction 3 to show how to construct flexible MSR RS codes.…”

Section: Conditionmentioning

confidence: 99%

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Storage Codes with Flexible Number of Nodes

Li¹,

Wang²,

Lu³

et al. 2021

Preprint

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This paper presents flexible storage codes, a class of error-correcting codes that can recover information from a flexible number of storage nodes. As a result, one can make a better use of the available storage nodes in the presence of unpredictable node failures and reduce the data access latency. Let us assume a storage system encodes kℓ information symbols over a finite field F into n nodes, each of size ℓ symbols.The code is parameterized by a set of tuplessuch that the information symbols can be reconstructed from any R j nodes, each node accessing ℓ j symbols. In other words, the code allows a flexible number of nodes for decoding to accommodate the variance in the data access time of the nodes. Code constructions are presented for different storage scenarios, including LRC (locally recoverable) codes, PMDS (partial MDS) codes, and MSR (minimum storage regenerating) codes. We analyze the latency of accessing information and perform simulations on Amazon clusters to show the efficiency of presented codes.

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Section: Flexible Msr Codesmentioning

confidence: 99%

Section: Conditionmentioning

confidence: 99%

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Storage Codes with Flexible Number of Nodes

Li¹,

Wang²,

Lu³

et al. 2021

Preprint

Self Cite

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“…• more than two errors in information symbol: In this case the complexity of procedures quantified by Υ 1I , Υ 2I , Υ 1P 1I can be added as the worst case: (16,6,23,9,2). (106)…”

Section: Complexity Estimation Of the Algorithmmentioning

confidence: 99%

“…More surprisingly, in recent years a new interest in these codes has risen in the research community. It is caused by the fact that they are extensively used in cloud technology, namely in distributed storage systems [5,6,7,8,9]. In [1] it was shown that RS codes could be extended not only three times, but even five times when they are constructed over certain finite fields.…”

Section: Introductionmentioning

confidence: 99%

Decoding Five Times Extended Reed Solomon Codes Using Syndromes

Farkas

Rakús

2020

cai

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Recently a new family of five times extended Reed Solomon codes constructed over certain finite fields GF (2 ζ ), where ζ ≥ 3 is an odd integer, was discovered. Until now only an erasure decoding algorithm for these codes was published. In this paper a new decoding algorithm is presented, which allows correcting up to two errors in a codeword from the five times extended Reed Solomon codes. The proposed decoding algorithm is based on syndrome usage.

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Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies

Funde

Swain

2022

IEEE Access

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The concept of big data security is introduced in this article along with many features. It illustrates the need for security in healthcare systems as the volume of data increases continuously over the period of time. The necessity of big data security as well as several big data analytics phases highlighted. It covers many big data privacy-preserving strategies. Many digital storage solutions being used in today's world are designed to work only with fixed format of the data. This paper introduces some methods for maintaining metadata obliviousness. The oblivious RAM technology mentioned in the research article address security concerns and it can be handled with the daily increase in data in several industries. Security needs are introduced at many phases of big data creation, such as information extraction, storage systems, and analytics of the information. Additionally, it presents several data recovery methods for recovering original data in the event of a data crash. This paper covers several data categorization methods for sorting data into normal and sensitive categories as well as methods for anomaly detection. It discusses the advantages and disadvantages of various security measures.INDEX TERMS Security, privacy, obliviousness, data recovery. I. INTRODUCTIONThe core concepts and phases of big data are introduced in this section. At several stages of big data production, such as data generation, data storage, and data analytics, security requirements are introduced. The importance of security in big data, cloud, and Internet of Things (IoT) infrastructure is discussed in this section. It discusses about several security measures that must be defended against various kinds of attacks. It discusses on the security of healthcare systems. It gives a general overview of the medical industry and discusses how patient data must be safeguarded if it is stored in a dispersed setting. A. BIG DATABig data is data that has large volume, heterogeneity, speed, and volatility all at the same time. The term "high volume" denotes to the significant quantity of data that is produced on a daily basis by a variety of companies and organizations. The rapid proliferation of new data types, many of which are going to be incorporated in current datasets that have been acquired from a variety of companies and devices, is referred to as heterogeneity. The term "high speed" refers to the rapid rate at which data is collected or captured from a variety of social networking sites into a database [1]. Huge amounts of data present additional difficulties to the security systems that are already in place due to the variety, volume, and unpredictability of the data. Most data storage models used today are designed to work with data that has been properly arranged. The currently available encryption methods are inefficient for use with massive data as once data has been encrypted, producing keys, encrypting it, and then decrypting it takes a significant amount of time. For instance, the data collected from patients, various sensors, soc...

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On the Sub-Packetization Size and the Repair Bandwidth of Reed-Solomon Codes

Cited by 18 publications

References 36 publications

Storage Codes with Flexible Number of Nodes

Storage Codes with Flexible Number of Nodes

Decoding Five Times Extended Reed Solomon Codes Using Syndromes

Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies

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