  Multi-access edge computing provides local resources in mobile networks as
the essential means for meeting the demands of emerging ultra-reliable
low-latency communications. At the edge, dynamic computing requests require
advanced resource management for adaptive network slicing, including resource
allocations, function scaling and load balancing to utilize only the necessary
resources in resource-constraint networks. Recent solutions are designed for a
static number of slices. Therefore, the painful process of optimization is
required again with any update on the number of slices. In addition, these
solutions intend to maximize instant rewards, neglecting long-term resource
scheduling. Unlike these efforts, we propose an algorithmic approach based on
multi-agent deep deterministic policy gradient (MADDPG) for optimizing resource
management for edge network slicing. Our objective is two-fold: (i) maximizing
long-term network slicing benefits in terms of delay and energy consumption,
and (ii) adapting to slice number changes. Through simulations, we demonstrate
that MADDPG outperforms benchmark solutions including a static slicing-based
one from the literature, achieving stable and high long-term performance.
Additionally, we leverage incremental learning to facilitate a dynamic number
of edge slices, with enhanced performance compared to pre-trained base models.
Remarkably, this approach yields superior reward performance while saving
approximately 90% of training time costs.
