  Anticipation for 6G's arrival comes with growing concerns about increased
energy consumption in computing and networking. The expected surge in connected
devices and resource-demanding applications presents unprecedented challenges
for energy resources. While sustainable resource allocation strategies have
been discussed in the past, these efforts have primarily focused on
single-domain orchestration or ignored the unique requirements posed by 6G. To
address this gap, we investigate the joint problem of service instance
placement and assignment, path selection, and request prioritization, dubbed
PIRA. The objective function is to maximize the system's overall profit as a
function of the number of concurrently supported requests while simultaneously
minimizing energy consumption over an extended period of time. In addition,
end-to-end latency requirements and resource capacity constraints are
considered for computing and networking resources, where queuing theory is
utilized to estimate the Age of Information (AoI) for requests. After
formulating the problem in a non-linear fashion, we prove its NP-hardness and
propose a method, denoted ORIENT. This method is based on the Double Dueling
Deep Q-Learning (D3QL) mechanism and leverages Graph Neural Networks (GNNs) for
state encoding. Extensive numerical simulations demonstrate that ORIENT yields
near-optimal solutions for varying system sizes and request counts.
