  The increase in data storage and power consumption at data-centers has made
it imperative to design energy efficient Distributed Storage Systems (DSS). The
energy efficiency of DSS is strongly influenced not only by the volume of data,
frequency of data access and redundancy in data storage, but also by the
heterogeneity exhibited by the DSS in these dimensions. To this end, we propose
and analyze the energy efficiency of a heterogeneous distributed storage system
in which $n$ storage servers (disks) store the data of $R$ distinct classes.
Data of class $i$ is encoded using a $(n,k_{i})$ erasure code and the (random)
data retrieval requests can also vary across classes. We show that the energy
efficiency of such systems is closely related to the average latency and hence
motivates us to study the energy efficiency via the lens of average latency.
Through this connection, we show that erasure coding serves the dual purpose of
reducing latency and increasing energy efficiency. We present a queuing
theoretic analysis of the proposed model and establish upper and lower bounds
on the average latency for each data class under various scheduling policies.
Through extensive simulations, we present qualitative insights which reveal the
impact of coding rate, number of servers, service distribution and number of
redundant requests on the average latency and energy efficiency of the DSS.
