  Nowadays, the increasing deployment of edge clouds globally provides users
with low-latency services. However, connecting an edge cloud to a core cloud
via optic cables in terrestrial networks poses significant barriers due to the
prohibitively expensive building cost of optic cables. Fortunately, emerging
Low Earth Orbit (LEO) satellite networks (e.g., Starlink) offer a more
cost-effective solution for increasing edge clouds, and hence large volumes of
data in edge clouds can be transferred to a core cloud via those networks for
time-sensitive big data tasks processing, such as attack detection. However,
the state-of-the-art satellite selection algorithms bring poor performance for
those processing via our measurements. Therefore, we propose a novel data
volume aware satellite selection algorithm, named DVA, to support such big data
processing tasks. DVA first takes into account both the data size in edge
clouds and satellite capacity to finalize the selection, thereby preventing
congestion in the access network and reducing transmitting duration. Extensive
simulations validate that DVA has a significantly lower average access network
duration than the state-of-the-art satellite selection algorithms in a LEO
satellite emulation platform.
