Combinatorial batch codes

In this paper, we study batch codes, which were introduced by Ishai, Kushilevitz, Ostrovsky and Sahai in [4]. A batch code specifies a method to distribute a database of $n$ items among $m$ devices (servers) in such a way that any $k$ items can be retrieved by reading at most $t$ items from each of the servers. It is of interest to devise batch codes that minimize the total storage, denoted by $N$, over all $m$ servers. We restrict out attention to batch codes in which every server stores a subset of the items. This is purely a combinatorial problem, so we call this kind of batch code a ''combinatorial batch code''. We only study the special case $t=1$, where, for various parameter situations, we are able to present batch codes that are optimal with respect to the storage requirement, $N$. We also study uniform codes, where every item is stored in precisely $c$ of the $m$ servers (such a code is said to have rate $1/c$). Interesting new results are presented in the cases $c = 2, k-2$ and $k-1$. In addition, we obtain improved existence results for arbitrary fixed $c$ using the probabilistic method.