Direct communication of optical network units (ONUs) is an important function in metro-access optical network. In this paper, a grid architecture supporting discretionary and efficient communication between ONUs is proposed. The grid architecture is built by using two interconnecting fibers to connect every two ONUs; this can give protection to every ONU by four ONUs.Additionally, the network reliability can be improved greatly. Grid topology of ONUs leads to their discretionary direct communication. In this topology, direct communication signal can be transmitted from one ONU to another ONU directly, rather than being returned to remote node central office. Also, the efficiency of direct communication can be improved remarkably with the proposed architecture. Finally, the performance analyses and the simulation results verify the feasibility of the proposed architecture. When the n reaches to 10, the reliability of downstream and direct communication signals is all larger than 99.993%.
To solve the problem of energy consumption optimization of edge servers in the process of edge task unloading, we propose a task unloading algorithm based on reinforcement learning in this paper. The algorithm observes and analyzes the current environment state, selects the deployment location of edge tasks according to current states, and realizes the edge task unloading oriented to energy consumption optimization. To achieve the above goals, we first construct a network energy consumption model including servers' energy consumption and link transmission energy consumption, which improves the accuracy of network energy consumption evaluation. Because of the complexity and variability of the edge environment, this paper designs a task unloading algorithm based on Proximal Policy Optimization (PPO), besides we use Dijkstra to determine the connection path between edge servers where adjacent tasks are deployed. Finally, lots of simulation experiments verify the effectiveness of the proposed method in the process of task unloading. Compared with contrast algorithms, the average energy saving of the proposed algorithm can reach 22.69%.
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