  This paper proposes an effective and novel multiagent deep reinforcement
learning (MADRL)-based method for solving the joint virtual network function
(VNF) placement and routing (P&R), where multiple service requests with
differentiated demands are delivered at the same time. The differentiated
demands of the service requests are reflected by their delay- and
cost-sensitive factors. We first construct a VNF P&R problem to jointly
minimize a weighted sum of service delay and resource consumption cost, which
is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative
subtasks: placement subtask and routing subtask. Each subtask consists of
multiple concurrent parallel sequential decision processes. By invoking the
deep deterministic policy gradient method and multi-agent technique, an
MADRL-P&R framework is designed to perform the two subtasks. The new joint
reward and internal rewards mechanism is proposed to match the goals and
constraints of the placement and routing subtasks. We also propose the
parameter migration-based model-retraining method to deal with changing network
topologies. Corroborated by experiments, the proposed MADRL-P&R framework is
superior to its alternatives in terms of service cost and delay, and offers
higher flexibility for personalized service demands. The parameter
migration-based model-retraining method can efficiently accelerate convergence
under moderate network topology changes.
