Two MDS Array Codes for Disk Erasures: the Blaum-Bruck-Vardy Code and the BASIC Code

In this post, I am going to review two erasure codes: the Blaum-Bruck-Vardy code and the BASIC code (also here). These are erasure codes, which means, their purpose is to encode a number of data disks into a number of coding disks so that when one or more data/coding disks fail, the failed disk can be reconstructed using the existing data and coding disks.

A strength of these codes is that although the algebra is described on extension fields/rings over GF(2), the encoding/decoding process uses only Boolean addition/rotation operation and no finite field operation. These codes are also MDS (Maximum Distance Separable), which means they have the largest possible (minimum) distance for a fixed message-length and codeword-length.

(Recall that if a code has d data components and c parity components in its generator matrix in standard form, its distance is at most c + 1 by the Singleton bound. Hence the code is MDS if and only if it can tolerate c arbitrary disk failures.)

The BASIC code does the following things in relations to the BBV code:

  1. Adds a virtual parity bit after each disk, giving each disk an even parity
  2. Does polynomial arithmetic modulo 1+x^p instead of h(x) = 1+x+\cdots + x^{p-1} as in the case of BBV code
  3. Shows equivalence to the BBV code by making a nice observation via Chinese Remainder Theorem
  4. Proves MDS property for any number of coding disks when p is “large enough” and has a certain structure

Open Question: What is the least disk size for which these codes are MDS with arbitrary distance?

Continue reading “Two MDS Array Codes for Disk Erasures: the Blaum-Bruck-Vardy Code and the BASIC Code”

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Generalized Vandermonde Matrices with Missing Row-Powers

Let [n] := \{0, 1, 2, \cdots, n -1 \}. Let x = (x_0, \cdots, x_{n-1}) with distinc x_js. Let R = \{0 = r_1, r_2, \cdots, r_{n-1} = r be a set of distinct nonnegative row-powers. Consider the n \times n Generalized Vandermonde matrix V(x ; R) := \left( x_j^{r_i} \right)_{i,j \in [n]}. When R = [n], this matrix becomes the ordinary Vandermonde matrix, V(x).

An equivalent description of R is the largest row-power r \in R and the set of missing rows from [r]: that is, the items that are in [r] but not in R. Let L_r = \{\ell_0, \ell_1, \cdots \} be this set. Define the punctured Generalized Vandermonde matrix V_{\perp}(x; L_r) := V(x; R). Let s_k(x) be the kth elementary symmetric polynomial.

Now we are ready to present some interesting results without proof, from the paper “Lower bound on Sequence Complexity” by Kolokotronis, Limniotis, and Kalouptsidis.

det V_{\perp}(x ; \{\ell \}) = det V(x) \ s_{n-\ell}(x).

det V_{\perp}(x ; \{\ell_0, \ell_1\}) = det V(x) \ det \left( s_{n-\ell_i+j}(x) \right)_{i,j \in [2]}.

det V_{\perp}(x ; L) = det V(x) \ det \left( s_{n-\ell_i+j}(x) \right)_{i,j \in [s]} \text{ where } L = \{ \ell_0, \ell_1, \cdots, \ell_{s-1}\}.

I will add some applications later on.