63 research outputs found
Derandomized Construction of Combinatorial Batch Codes
Combinatorial Batch Codes (CBCs), replication-based variant of Batch Codes
introduced by Ishai et al. in STOC 2004, abstracts the following data
distribution problem: data items are to be replicated among servers in
such a way that any of the data items can be retrieved by reading at
most one item from each server with the total amount of storage over
servers restricted to . Given parameters and , where and
are constants, one of the challenging problems is to construct -uniform CBCs
(CBCs where each data item is replicated among exactly servers) which
maximizes the value of . In this work, we present explicit construction of
-uniform CBCs with data items. The
construction has the property that the servers are almost regular, i.e., number
of data items stored in each server is in the range . The
construction is obtained through better analysis and derandomization of the
randomized construction presented by Ishai et al. Analysis reveals almost
regularity of the servers, an aspect that so far has not been addressed in the
literature. The derandomization leads to explicit construction for a wide range
of values of (for given and ) where no other explicit construction
with similar parameters, i.e., with , is
known. Finally, we discuss possibility of parallel derandomization of the
construction
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
Multiset Combinatorial Batch Codes
Batch codes, first introduced by Ishai, Kushilevitz, Ostrovsky, and Sahai,
mimic a distributed storage of a set of data items on servers, in such
a way that any batch of data items can be retrieved by reading at most some
symbols from each server. Combinatorial batch codes, are replication-based
batch codes in which each server stores a subset of the data items.
In this paper, we propose a generalization of combinatorial batch codes,
called multiset combinatorial batch codes (MCBC), in which data items are
stored in servers, such that any multiset request of items, where any
item is requested at most times, can be retrieved by reading at most
items from each server. The setup of this new family of codes is motivated by
recent work on codes which enable high availability and parallel reads in
distributed storage systems. The main problem under this paradigm is to
minimize the number of items stored in the servers, given the values of
, which is denoted by . We first give a necessary and
sufficient condition for the existence of MCBCs. Then, we present several
bounds on and constructions of MCBCs. In particular, we
determine the value of for any , where
is the maximum size of a binary constant weight code of length
, distance four and weight . We also determine the exact value of
when or
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