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Management and orchestration (MANO) of resources by virtual network functions(VNFs) represents one of the key challenges towards a fully virtualized networkarchitecture as envisaged by 5G standards. Current threshold-based policiesinefficiently over-provision network resources and under-utilize availablehardware, incurring high cost for network operators, and consequently, theusers. In this work, we present a MANO algorithm for VNFs allowing a centralunit (CU) to learn to autonomously re-configure resources (processing power andstorage), deploy new VNF instances, or offload them to the cloud, depending onthe network conditions, available pool of resources, and the VNF requirements,with the goal of minimizing a cost function that takes into account theeconomical cost as well as latency and the quality-of-service (QoS) experiencedby the users. First, we formulate the stochastic resource optimization problemas a parameterized action Markov decision process (PAMDP). Then, we propose asolution based on deep reinforcement learning (DRL). More precisely, we presenta novel RL approach called, parameterized action twin (PAT) deterministicpolicy gradient, which leverages an actor-critic architecture to learn toprovision resources to the VNFs in an online manner. Finally, we presentnumerical performance results, and map them to 5G key performance indicators(KPIs). To the best of our knowledge, this is the first work that considers DRLfor MANO of VNFs' physical resources.

Management and Orchestration of Virtual Network Functions via Deep Reinforcement Learning