A control and data plane split approach for partial offloading in mobile fog networks

Fog Computing offers storage and computational capabilities to the edge devices by reducing the traffic at the fronthaul. A fog environment can be seen as composed by two main classes of devices, Fog Nodes (FNs) and Fog-Access Points (F-APs). At the same time, one of the major advances in 5G systems is decoupling the control and the data planes. With this in mind we are here proposing an optimization technique for a mobile environment where the Device to Device (D2D) communications between FNs act as a control plane for aiding the computational offloading traffic operating on the data plane composed by the FN - F-AP links. Interactions in the FNs layer are used for exchanging the information about the status of the F-AP to be exploited for offloading the computation. With this knowledge, we have considered the mobility of FNs and the F-APs' coverage areas to propose a partial offloading approach where the amount of tasks to be offloaded is estimated while the FNs are still within the coverage of their F-APs. Numerical results show that the proposed approaches allow to achieve performance closer to the ideal case, by reducing the data loss and the delay

Computation Offloading and Resource Allocation for Backhaul Limited Cooperative MEC Systems

In this paper, we jointly optimize computation offloading and resource allocation to minimize the weighted sum of energy consumption of all mobile users in a backhaul limited cooperative MEC system with multiple fog servers. Considering the partial offloading strategy and TDMA transmission at each base station, the underlying optimization problem with constraints on maximum task latency and limited computation resource at mobile users and fog servers is non-convex. We propose to convexify the problem exploiting the relationship among some optimization variables from which an optimal algorithm is proposed to solve the resulting problem. We then present numerical results to demonstrate the significant gains of our proposed design compared to conventional designs without exploiting cooperation among fog servers and a greedy algorithm

Collaborative Vehicular Edge Computing Networks: Architecture Design and Research Challenges

The emergence of augmented reality (AR), autonomous driving and other new applications have greatly enriched the functionality of the vehicular networks. However, these applications usually require complex calculations and large amounts of storage, which puts tremendous pressure on traditional vehicular networks. Mobile edge computing (MEC) is proposed as a prospective technique to extend computing and storage resources to the edge of the network. Combined with MEC, the computing and storage capabilities of the vehicular network can be further enhanced. Therefore, in this paper, we explore the novel collaborative vehicular edge computing network (CVECN) architecture. We first review the work related to MEC and vehicular networks. Then we discuss the design principles of CVECN. Based on the principles, we present the detailed CVECN architecture, and introduce the corresponding functional modules, communication process, as well as the installation and deployment ideas. Furthermore, the promising technical challenges, including collaborative coalition formation, collaborative task offloading and mobility management, are presented. And some potential research issues for future research are highlighted. Finally, simulation results are verified that the proposed CVECN can significantly improve network performance

Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing

In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. A key characteristic of D2D networks is that their topologies are reconfigurable to cope with network demands. For distributed computing, resource management is challenging due to limited network and communication resources, leading to inter-channel interference. To overcome this, recent research has addressed the problems of wireless scheduling, subchannel allocation, power allocation, and multiple-input multiple-output (MIMO) signal design, but has not considered them jointly. In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency. Given that the resulting problem is a non-convex mixed integer program (MIP) which is prohibitive to solve at scale, we decompose its solution into two parts: (i) a resource allocation subproblem, which optimizes the link selection and subchannel allocations, and (ii) MIMO signal design subproblem, which optimizes the transmit beamformer, transmit power, and receive combiner. Simulation results using wireless edge topologies show that our method yields substantial improvements in energy efficiency compared with cases of no offloading and partially optimized methods and that the efficiency scales well with the size of the network.Comment: 10 pages, 7 figures, Accepted by INFOCOM 202