  Virtualization technologies are the foundation of modern ICT infrastructure,
enabling service providers to create dedicated virtual networks (VNs) that can
support a wide range of smart city applications. These VNs continuously
generate massive amounts of data, necessitating stringent reliability and
security requirements. In virtualized network environments, however, multiple
VNs may coexist on the same physical infrastructure and, if not properly
isolated, may interfere with or provide unauthorized access to one another. The
former causes performance degradation, while the latter compromises the
security of VNs. Service assurance for infrastructure providers becomes
significantly more complicated when a specific VN violates the isolation
requirement.
  In an effort to address the isolation issue, this paper proposes isolation
during virtual network embedding (VNE), the procedure of allocating VNs onto
physical infrastructure. We define a simple abstracted concept of isolation
levels to capture the variations in isolation requirements and then formulate
isolation-aware VNE as an optimization problem with resource and isolation
constraints. A deep reinforcement learning (DRL)-based VNE algorithm
ISO-DRL_VNE, is proposed that considers resource and isolation constraints and
is compared to the existing three state-of-the-art algorithms: NodeRank, Global
Resource Capacity (GRC), and Mote-Carlo Tree Search (MCTS). Evaluation results
show that the ISO-DRL_VNE algorithm outperforms others in acceptance ratio,
long-term average revenue, and long-term average revenue-to-cost ratio by 6%,
13%, and 15%.
