  Modern satellites deployed in low Earth orbit (LEO) accommodate processing
payloads that can be exploited for edge computing. Furthermore, by implementing
inter-satellite links, the LEO satellites in a constellation can route the data
end-to-end (E2E). These capabilities can be exploited to greatly improve the
current store-and-forward approaches in Earth surveillance systems. However,
they give rise to an NP-hard problem of joint communication and edge computing
resource management (RM). In this paper, we propose an algorithm that allows
the satellites to select between computing the tasks at the edge or at a cloud
server and to allocate an adequate power for communication. The overall
objective is to minimize the energy consumption at the satellites while
fulfilling specific service E2E latency constraints for the computing tasks.
Experimental results show that our algorithm achieves energy savings of up to
18% when compared to the selected benchmarks with either 1) fixed edge
computing decisions or 2) maximum power allocation.
