The emergence of a new generation of applicationsled to the appearance of new challenges that represent improve-
ments in current communication technologies. For this, a new
network paradigm’s including edge computing that allows the
process of data at the edge of the network. And the 5G network
slicing that represents a new generation of communication in-
creases the capacity of mobile networks by supporting the slicing
technology that allows virtual ”cutting” of a telecommunications
network in several slices that provide high performance in terms
of bandwidth and latency. Slice allocation and placement is an
important networking optimization task that still painstakingly
tune heuristics to get a suf?cient solution. These algorithms use
data as input and outputs near-optimal solutions. Thus, we are
motivated by replacing this tedious process with the recent deep
reinforcement learning algorithms. In this paper, we propose
three approaches for Virtual Network Functions (VNFs) slices
placement in edge computing (Integer linear programming (ILP),
reinforcement learning (RL), and deep reinforcement learning
(DRL)). Then they are implemented and evaluated. Several
scenarios are considered to study the behavior of the algorithms
and to quantify the impact of network size. The results show the
feasibility and ef?ciency of the proposed techniques in terms of
server utilization, placement time, and energy consumptio
