  We are entering a rapidly unfolding future driven by the delivery of
real-time computation services, such as industrial automation and augmented
reality, collectively referred to as AgI services, over highly distributed
cloud/edge computing networks. The interaction intensive nature of AgI services
is accelerating the need for networking solutions that provide strict latency
guarantees. In contrast to most existing studies that can only characterize
average delay performance, we focus on the critical goal of delivering AgI
services ahead of corresponding deadlines on a per-packet basis, while
minimizing overall cloud network operational cost. To this end, we design a
novel queuing system able to track data packets' lifetime and formalize the
delay-constrained least-cost dynamic network control problem. To address this
challenging problem, we first study the setting with average capacity (or
resource budget) constraints, for which we characterize the delay-constrained
stability region and design a near-optimal control policy leveraging Lyapunov
optimization theory on an equivalent virtual network. Guided by the same
principle, we tackle the peak capacity constrained scenario by developing the
reliable cloud network control (RCNC) algorithm, which employs a two-way
optimization method to make actual and virtual network flow solutions converge
in an iterative manner. Extensive numerical results show the superior
performance of the proposed control policy compared with the state-of-the-art
cloud network control algorithm, and the value of guaranteeing strict
end-to-end deadlines for the delivery of next-generation AgI services.
