As two of the key technologies of 5G, fog-based radio access network and network function virtualization have become an important direction for the radio network architecture evolution. Virtual network functions (VNFs) compose the service function chain (SFC) in a particular order, and the mobile network users communicate with each other or service terminals through SFCs. For service providers, it is crucially important for efficient deploying/mapping SFCs into the 5G mobile network since the SFCs deployment problem is an NP-hard problem. In this paper, we propose the efficient SFCs deployment algorithms for solving this challenge with two main design goals: 1) minimizing the cost of link resource, i.e., minimizing the path length of the entire SFC by combining VNFs and mapping temporary links and 2) minimizing the cost of computing resources by using the existing virtual machines as much as possible while performing the same VNF. We model the SFCs deployment problem as an optimization problem by using ILP as well as devise the heuristic algorithms to make the tradeoff between these two conflicting design goals. From simulation results, we can see that the performance of our proposed algorithms is promising in terms of the total SFCs mapping cost, total links mapping cost, and blocking ratio. INDEX TERMS Service function chain, fog-based radio access network, virtual network function, network function virtualization, deployment, 5G mobile network.
Theory of mind (ToM) is the ability to attribute mental states to oneself and others, and to understand that others have beliefs that are different from one's own. Although functional neuroimaging techniques have been widely used to establish the neural correlates implicated in ToM, the specific mechanisms are still not clear. We make our efforts to integrate and adopt existing biological findings of ToM, bridging the gap through computational modeling, to build a brain-inspired computational model for ToM. We propose a Brain-inspired Model of Theory of Mind (Brain-ToM model), and the model is applied to a humanoid robot to challenge the false belief tasks, two classical tasks designed to understand the mechanisms of ToM from Cognitive Psychology. With this model, the robot can learn to understand object permanence and visual access from self-experience, then uses these learned experience to reason about other's belief. We computationally validated that the self-experience, maturation of correlate brain areas (e.g., calculation capability) and their connections (e.g., inhibitory control) are essential for ToM, and they have shown their influences on the performance of the participant robot in false-belief task. The theoretic modeling and experimental validations indicate that the model is biologically plausible, and computationally feasible as a foundation for robot theory of mind.
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