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Assigning the resources of a virtual network to the components of a physical network, called Virtual Network Mapping, plays a central role in network virtualization. Existing approaches use classical heuristics like simulated annealing or attempt a two stage solution by solving the node mapping in a first stage and doing the link mapping in a second stage.The contribution of this paper is a Virtual Network Mapping (VNM) algorithm based on subgraph isomorphism detection: it maps nodes and links during the same stage. Our experimental evaluations show that this method results in better mappings and is faster than the two stage approach, especially for large virtual networks with high resource consumption which are hard to map.

Abstract-Network appliances perform different functions on network flows and constitute an important part of an operator's network. Normally, a set of chained network functions process network flows. Following the trend of virtualization of networks, virtualization of the network functions has also become a topic of interest. We define a model for formalizing the chaining of network functions using a context-free language. We process deployment requests and construct virtual network function graphs that can be mapped to the network. We describe the mapping as a Mixed Integer Quadratically Constrained Program (MIQCP) for finding the placement of the network functions and chaining them together considering the limited network resources and requirements of the functions. We have performed a Pareto set analysis to investigate the possible trade-offs between different optimization objectives.

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This paper studies how to schedule wireless transmissions from sensors to estimate the states of multiple remote, dynamic processes. Sensors make observations of each of the processes. Information from the different sensors have to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. Such estimation problems routinely occur in large-scale Cyber-Physical Systems, especially when the dynamic systems (processes) involved are geographically separated. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant to decide which sensors have network access and which ones do not. To solve this scheduling problem, we formulate an associated Markov decision process (MDP). Further, we solve this MDP using a Deep Q-Network, a deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waitingtime, among others. Our algorithm is shown to significantly outperform these algorithms for randomly generated example scenarios.arXiv:1809.05149v1 [cs.SY]

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