Reliable modeling of proton exchange membrane fuel cell (PEMFC) degradation is essential for improving system reliability and enabling health-aware control. This paper proposes a two-dimensional multiphysics aging model that captures the dynamic evolution of Pt particles by integrating agglomeration, migration, dissolution, and redeposition mechanisms. Furthermore, a health index (HI) is derived from the particle-scale degradation behavior to real-time quantify the latent aging state of fuel cells. The proposed model and HI are validated under both accelerated stress tests and real driving cycles. The results show that the HI under static voltage is 0.012, and the HI under fluctuating voltage increases to 0.105, reflecting that voltage transients exacerbate the spatial imbalance of reaction intensity and performance loss, providing a model basis for health-aware energy management system.

The strategy for 40/100Gbps Energy Efficient Ethernet (EEE) determines when to enter and leave the power-saving modes. Accordingly, it directly decides both the energy savings and the incurred latency of frames in the EEE. The performance of the EEE strategy is greatly influenced by the network traffic, and thus existing EEE strategies need either proper parameter configuration under certain traffic loads or parameter adaptation mechanisms based on the traffic prediction under the assumptions of certain distribution. Consequently, these EEE strategies hardly keep consistent high performance under variable traffic in reality. To address this issue, we bring the reinforcement learning method into the design of the EEE strategy and propose the reinforcement learning based periodic strategy (RLPS) in this paper. Specifically, RLPS transmits existing frames at first and then stays in the selected power-saving mode for the rest time in each cycle. Moreover, RLPS learns the time length of each cycle online to reflect the impacts of traffic, instead of directly outputting power-saving mode transition decisions. In this way, the power consumption in each cycle is optimal with the help of learned information, and the overhead of online learning is reduced. Simulations driven by both synthetic traffic and real traces confirm that RLPS outperforms existing strategies, i.e., can achieve consistent high performance regardless of the traffic loads and distributions.

Nowadays, the RDMA (Remote Direct Memory Access) technology has been broadly employed in data centers. The Shared Receive Queue (SRQ) is an embedded mechanism in RDMA protocol, which reduces the memory cost of queue pairs sharing the same receiver. However, the configurations of SRQ are often heuristic and empirical nowadays. Consequently, the Receiver Not Ready (RNR) signal would be easily triggered, leading to utilization loss in the face of dynamic traffic. In other words, configuring SRQ reasonably is the key to the performance of RDMA and remains a challenge due to the variable traffic and environment. To address this issue, we propose a theoretical model for SRQ to guide its configuration. Simulations demonstrate that the system utilization is significantly improved and the triggering of RNR signals is reduced with the proper SRQ configuration guided by the theoretical model.