  This paper investigates the semantic extraction task-oriented dynamic
multi-time scale user admission and resourceallocation in mobile edge computing
(MEC) systems. Amid prevalence artifi cial intelligence applications in various
industries,the offloading of semantic extraction tasks which are mainlycomposed
of convolutional neural networks of computer vision isa great challenge for
communication bandwidth and computing capacity allocation in MEC systems.
Considering the stochasticnature of the semantic extraction tasks, we formulate
a stochastic optimization problem by modeling it as the dynamic arrival of
tasks in the temporal domain. We jointly optimize the system revenue and cost
which are represented as user admission in the long term and resource
allocation in the short term respectively. To handle the proposed stochastic
optimization problem, we decompose it into short-time-scale subproblems and a
long-time-scale subproblem by using the Lyapunov optimization technique. After
that, the short-time-scale optimization variables of resource allocation,
including user association, bandwidth allocation, and computing capacity
allocation are obtained in closed form. The user admission optimization on
long-time scales is solved by a heuristic iteration method. Then, the
multi-time scale user admission and resource allocation algorithm is proposed
for dynamic semantic extraction task computing in MEC systems. Simulation
results demonstrate that, compared with the benchmarks, the proposed algorithm
improves the performance of user admission and resource allocation efficiently
and achieves a flexible trade-off between system revenue and cost at multi-time
scales and considering semantic extraction tasks.
