FPGA-based acceleration has been emerged to avoid the cloud computing overload problem by accelerating the compute-intensive workload on edge networks. Though existing studies for FPGA-based edge acceleration have focused on optimizing the computing time, they did not address the burden of the FPGA-aided server under an enormous computing requests circumstance. The massive computing requests can cause delays in transmission and processing at the FPGA-aided server, leading to long response times and high system energy consumption. Therefore, we propose an emerging edge acceleration architecture based on FPGA cluster over the low-latency RDMA-based network. Preliminary simulation results demonstrate that our architecture is fast, scalable, and energy-efficient in comparison with the FPGA-aided servers cluster.

Device-to-device (D2D) communication is a promising technique for traffic offloading in next-generation cellular systems. In this paper, we study the D2D-assisted cellular traffic offloading (DACTO) problem, where Wi-Fi Direct technology is employed in D2D communication in consideration of its wide communication coverage and high transmission rate. Taking into account the user traffic demands and population distributions, we formulate the DACTO problem as a "Min-Max" problem, in which the operator energy consumption is minimized and meanwhile the user satisfaction is maximized. The DACTO is proven to be a NP-complete problem and is difficult to tackle with the increasing number of population. To achieve a feasible solution, we convert the DACTO problem into an approximate combination optimization problem, and develop a backpack algorithm combined with an improved Hungarian algorithm to solve it. Simulation results show that the proposed method achieves the near-optimal solution for the DACTO problem.