1 research outputs found
Queued cross-bar network models for replication and coded storage systems
Coding techniques may be useful for data center data survivability as well as
for reducing traffic congestion. We present a queued cross-bar network (QCN)
method that can be used for traffic analysis of both replication/uncoded and
coded storage systems. We develop a framework for generating QCN rate regions
(RRs) by analyzing their conflict graph stable set polytopes (SSPs). In doing
so, we apply recent results from graph theory on the characterization of
particular graph SSPs. We characterize the SSP of QCN conflict graphs under a
variety of traffic patterns, allowing for their efficient RR computation. For
uncoded systems, we show how to compute RRs and find rate optimal scheduling
algorithms. For coded storage, we develop a RR upper bound, for which we
provide an intuitive interpretation. We show that the coded storage RR upper
bound is achievable in certain coded systems in which drives store sufficient
coded information, as well in certain dynamic coding systems. Numerical
illustrations show that coded storage can result in gains in RR volume of
approximately 50%, averaged across traffic patterns