Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Information about uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this paper proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure

Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Information about uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this paper proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL scheme.Comment: 12 pages, 2 figure

Low-Complexity Power Allocation for Network-Coded User Scheduling in Fog-RANs

Consider a Fog Radio Access Network (FRAN) in which a cloud base station (CBS) is responsible for scheduling user-equipments (UEs) to a set of radio resource blocks (RRBs) of Fog Access Points (F-APs) and for allocating power to the RRBs. The conventional graphical approach for solving the coordinated scheduling and power control problem in FRAN requires prohibitive computational complexity. This letter, instead, proposes a low-complexity solution to the problem under the constraint that all the scheduled UEs can decode the requested files sent by their associated RRBs/F-APs. Unlike previous solution that requires constructing the total power control graph, the proposed computationally efficient solution is developed using a single power control subgraph. Numerical results reveal a close-to-optimal performance of the proposed method in terms of throughput maximization for correlated channels with a significant reduction in the computational complexity

Zenith: Utility-Aware Resource Allocation for Edge Computing

In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between the IoT devices and the back-end computing infrastructure. In the edge computing model, small-scale micro-datacenters that represent ad-hoc and distributed collection of computing infrastructure pose new challenges in terms of management and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose Zenith, a novel model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori. Based on the established contracts, service providers employ a latency-aware scheduling and resource provisioning algorithm that enables tasks to complete and meet their latency requirements. The proposed techniques are evaluated through extensive experiments that demonstrate the effectiveness, scalability and performance efficiency of the proposed model

The Next Generation Internet of Things Architecture Towards Distributed Intelligence: Reviews, Applications, and Research Challenges

Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from the raw data derived from sensors. In the current cloud computing architecture, all the IoT raw data are transmitted to the cloud for processing, storage, and controlling things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by providing IoT Gateway at the edge layer with the required intelligence to gain the knowledge from raw data to decide whether to actuate or offload complicated tasks to the cloud. This collaboration between the cloud and the edge is called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between the cloud and the edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability

Energy Efficient Resource Allocation Optimization in Fog Radio Access Networks with Outdated Channel Knowledge

Fog Radio Access Networks (F-RAN) are gaining worldwide interests for enabling mobile edge computing for Beyond 5G. However, to realize the future real-time and delay-sensitive applications, F-RAN tailored radio resource allocation and interference management become necessary. This work investigates user association and beamforming issues for providing energy efficient F-RANs. We formulate the energy efficiency maximization problem, where the F-RAN specific constraint to guarantee local edge processing is explicitly considered. To solve this intricate problem, we design an algorithm based on the Augmented Lagrangian (AL) method. Then, to alleviate the computational complexity, a heuristic low-complexity strategy is developed, where the tasks are split in two parts: one solving for user association and Fog Access Points (F-AP) activation in a centralized manner at the cloud, based on global but outdated user Channel State Information (CSI) to account for fronthaul delays, and the second solving for beamforming in a distributed manner at each active F-AP based on perfect but local CSIs. Simulation results show that the proposed heuristic method achieves an appreciable performance level as compared to the AL-based method, while largely outperforming the energy efficiency of the baseline F-RAN scheme and limiting the sum-rate degradation compared to the optimized sum-rate maximization algorithm