  Distributed data processing platforms (e.g., Hadoop, Spark, and Flink) are
widely used to distribute the storage and processing of data among computing
nodes of a cloud. The centralization of cloud resources has given birth to edge
computing, which enables the processing of data closer to the data source
instead of sending it to the cloud. However, due to resource constraints such
as energy limitations, edge computing cannot be used for deploying all kinds of
applications. Therefore, tasks are offloaded from an edge device to the more
resourceful cloud. Previous research has evaluated the energy consumption of
the distributed data processing platforms in the isolated cloud and edge
environments. However, there is a paucity of research on evaluating the energy
consumption of these platforms in an integrated edge-cloud environment, where
tasks are offloaded from a resource-constraint device to a resource-rich
device. Therefore, in this paper, we first present a framework for the
energy-aware evaluation of the distributed data processing platforms. We then
leverage the proposed framework to evaluate the energy consumption of the three
most widely used platforms (i.e., Hadoop, Spark, and Flink) in an integrated
edge-cloud environment consisting of Raspberry Pi, edge node, edge server node,
private cloud, and public cloud. Our evaluation reveals that (i) Flink is most
energy-efficient followed by Spark and Hadoop is found least energy-efficient
(ii) offloading tasks from resource-constraint to resource-rich devices reduces
energy consumption by 55.2%, and (iii) bandwidth and distance between client
and server are found key factors impacting the energy consumption.
