Forward Error Correction Raid
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Clustered ONTAP for example has evolved from being an OS for HA-pair of storage controllers to a clustered system as a result of reed solomon example integration with Spinnaker intellectual property. It's not necessarily bad, because cDOT shows
Erasure Code
better performance on SPECsfs2008 than Isilon, but these systems still have two core architectural differences: 1. Isilon doesn't have mds code RAIDs and complexities associated with them. You don't choose RAID protection level. You don't need to think about RAID groups and even load distribution between them. You don't even error correcting codes have spare drives per se. 2. All data on Isilon system is kept on one volume, which is a one big distributed file system. cDOT use concept of infinite volumes, but bear in mind that each NetApp filer has it's own file system beneath. If you have 24 NetApp nodes in a cluster, then you have 24 underlying
Raid 6
file systems, even though they are viewed as a whole from the client standpoint. This makes Isilon very easy to configure and operate. But its simplicity comes at a price of flexibility. Isilon web interface has few options to configure and not very feature rich. Isilon Nodes and Networking In a nutshell Isilon is a collection of a certain number of nodes connected via 20Gb/s DDR InfiniBand back-end network and either 1GB/s or 10GB/s front-end network for client connections. There are three types of Isilon nodes S-Series (SAS + SSD drives) for transactional random access I/O, X-Series (SATA + SSD drives) for high throughput applications and NL-series (SATA drives) for archival or not frequently used data. If you choose to have two IB switches at the back-end, then you'll have three subnets configured for internal network: int-a, int-b and failover. You can think of a failover network as a virtual network in front of int-a and int-b. So when the packet comes to failover network IP address, the actual IB interf
BCH code Reed–Solomon code Block length n Message length k Distance n − k + 1 Alphabet size q = pm ≥ n (p prime) Often n = q − 1. Notation [n, k, n − k + 1]q-code Algorithms Decoding Berlekamp–Massey Euclidean et al. Properties Maximum-distance separable code v t e Reed–Solomon codes are a group of error-correcting codes that were introduced by Irving S. Reed and Gustave Solomon in 1960.[1] They have many applications, the most prominent of which include consumer technologies such as CDs, DVDs, Blu-ray Discs, QR Codes, data transmission technologies such as DSL and WiMAX, https://niktips.wordpress.com/tag/forward-error-correction/ broadcast systems such as DVB and ATSC, and storage systems such as RAID 6. They are also used in satellite communication. In coding theory, the Reed–Solomon code belongs to the class of non-binary cyclic error-correcting codes. The Reed–Solomon code is based on univariate polynomials over finite fields. It is able to detect and correct multiple symbol errors. By adding t check symbols to the data, a https://en.wikipedia.org/wiki/Reed%E2%80%93Solomon_error_correction Reed–Solomon code can detect any combination of up to t erroneous symbols, or correct up to ⌊t/2⌋ symbols. As an erasure code, it can correct up to t known erasures, or it can detect and correct combinations of errors and erasures. Furthermore, Reed–Solomon codes are suitable as multiple-burst bit-error correcting codes, since a sequence of b+1 consecutive bit errors can affect at most two symbols of size b. The choice of t is up to the designer of the code, and may be selected within wide limits. Contents 1 History 2 Applications 2.1 Data storage 2.2 Bar code 2.3 Data transmission 2.4 Space transmission 3 Constructions 3.1 Reed & Solomon's original view: The codeword as a sequence of values 3.1.1 Simple encoding procedure: The message as a sequence of coefficients 3.1.2 Systematic encoding procedure: The message as an initial sequence of values 3.1.3 Theoretical decoding procedure 3.2 The BCH view: The codeword as a sequence of coefficients 3.2.1 Systematic encoding procedure 3.3 Duality of the two views - discrete Fourier transform 3.4 Remarks 4 Properties 5 Error correction algorithms 5.1 Peterson–Gorenstein–Zierler decoder 5.1.1 Syndrome decoding 5.1.2 Error locators and error values 5.1.3 Error lo
Networking Smart grid Hamming code Definition Hamming code Facebook Like Tweet Google +1 LinkedIn Email Comment RSS Print A AA AAA Part of the Smart grid glossary: Hamming code is a set of error-correction code s that can http://whatis.techtarget.com/definition/Hamming-code be used to detect and correct bit errors that can occur when computer data is moved or stored. Hamming code is named for R. W. Hamming of Bell Labs. Like other error-correction code, Hamming code makes use of the concept of parity and parity bit s, which are bits that are added to data so that the validity of the data can be checked when it is read or after it has been received in a data transmission. Using more than one parity bit, an error-correction code can forward error not only identify a single bit error in the data unit, but also its location in the data unit. In data transmission, the ability of a receiving station to correct errors in the received data is called forward error correction (FEC) and can increase throughput on a data link when there is a lot of noise present. To enable this, a transmitting station must add extra data (called error correction bits ) to the transmission. However, the correction may not always represent a cost saving over that forward error correction of simply resending the information. Hamming codes make FEC less expensive to implement through the use of a block parity mechanism. Computing parity involves counting the number of ones in a unit of data, and adding either a zero or a one (called a parity bit ) to make the count odd (for odd parity) or even (for even parity). For example, 1001 is a 4-bit data unit containing two one bits; since that is an even number, a zero would be added to maintain even parity, or, if odd parity was being maintained, another one would be added. To calculate even parity, the XOR operator is used; to calculate odd parity, the XNOR operator is used. Single bit errors are detected when the parity count indicates that the number of ones is incorrect, indicating that a data bit has been flipped by noise in the line. Hamming codes detect two bit errors by using more than one parity bit, each of which is computed on different combinations of bits in the data. The number of parity bits required depends on the number of bits in the data transmission, and is calculated by the Hamming rule: pd + p + 1 < = 2 (1) Where d is the number of data bits and p is the number of parity bits. The total of the two is called the Hamming code word, which is generated by multiplying the data bits by a generator matrix .
This was last updated in March 2010 Contributor(s): Malathi Ram Posted by: Margaret Rouse R