  In cloud storage systems with a large number of servers, files are typically
not stored in single servers. Instead, they are split, replicated (to ensure
reliability in case of server malfunction) and stored in different servers. We
analyze the mean latency of such a split-and-replicate cloud storage system
under general sub-exponential service time. We present a novel scheduling
scheme that utilizes the load-balancing policy of the \textit{power of $d$
$(\geq 2)$} choices. An alternative to split-and-replicate is to use
erasure-codes, and recently, it has been observed that they can reduce latency
in data access (see \cite{longbo_delay} for details). We argue that under high
redundancy (integer redundancy factor strictly greater than or equal to 2)
regime, the mean latency of a coded system is upper bounded by that of a
split-and-replicate system (with same replication factor) and the gap between
these two is small. We validate this claim numerically under different service
distributions such as exponential, shift plus exponential and the heavy-tailed
Weibull distribution and compare the mean latency to that of an
unsplit-replicated system. We observe that the coded system outperforms the
unsplit-replication system by at least $20\%$. Furthermore, we consider the
mean latency for an erasure coded system with low redundancy (fractional
redundancy factor between 1 and 2), a scenario which is more pragmatic, given
the storage constraints (\cite{rashmi_thesis}). However under this regime, we
restrict ourselves to the special case of exponential service time distribution
and use the randomized load balancing policy namely \textit{batch-sampling}. We
obtain an upper bound on mean delay that depends on the order statistics of the
queue lengths, which, we further smooth out via a discrete to continuous
approximation.
