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Minimum Interference Routing is instrumental to MPLS Traffic Engineering under realistic assumptions of unknown traffic demand. This work presents a new algorithm for minimum interference routing, called Light Minimum Interference Routing (LMIR). This algorithm introduces a new approach for critical link identification that reduces the computational complexity. Results, derived via simulation, show that LMIR is precise and has indeed a low computational complexity.

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Cloud Radio Access Networks (CRAN) allow to reduce power consumption in future 5G networks by decoupling BaseBand Units (BBU) from cell sites and centralizing the baseband processing from Remote Radio-Heads (RRH) in BBUs pools in a cloud. Although this centralization can enable power savings, it imposes much higher traffic on the optical transport network used to connect RRHs to the BBU pool, i.e., the fronthaul. In this paper we propose a hybrid Cloud-Fog RAN (CF-RAN) architecture that resorts to fog computing and to Network Functions Virtualization (NFV) to replicate the processing capacity of CRAN in local fog nodes closer to the RRHs that can be activated on demand to process surplus fronthaul/cloud traffic. We devise an ILP formulation and graph-based heuristics to decide when to activate fog nodes and how to dimension wavelengths on a Timeand-Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) to support the fronthaul. Our results show that our architecture can consume up to 96% less energy than a traditional Distributed RAN (DRAN), providing a maximum transmission latency of about 20µs between RRHs and BBUs even in large traffic scenarios. Moreover, we demonstrate that our graph-based heuristics can achieve same optimal solutions of the ILP formulation but with a reduction of 99.86% in the execution time.