Nowadays, the rise of various network applications makes network traffic become increasingly complex, which brings more stringent requirements to traffic engineering (TE). Although the state-of-the-art TE approaches b...
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ISBN:
(纸本)9798350387117;9798350387124
Nowadays, the rise of various network applications makes network traffic become increasingly complex, which brings more stringent requirements to traffic engineering (TE). Although the state-of-the-art TE approaches based on deep reinforcement learning (DRL) or traditional methods can generate optimal solutions for fixed traffic matrices, they cannot converge fast enough to provide real-time optimization in real networks either because of excessive computation times or high communication overheads. Moreover, due to the dynamically changing traffic load on the network, it is also challenging to achieve optimization of maximum link utilization (MLU) and end-to-end delay at the same time since these two optimization objectives may be conflicting, especially when the network is under a low traffic load, which makes the modeling very difficult. To meet these challenges, we present RT-TE, a TE system based on DRL and distributed message-passing between intelligent agents that can achieve real-time optimization for both MLU and end-to-end delay. To reduce the communication time due to link propagation delay during the optimization process, we design a proactive message-passing mechanism that allows agents to use partial messages to compute the routing policy while maintaining the optimization performance. Additionally, to achieve the tradeoff between the two optimization objectives, we model the propagation delay into the DRL model and design a multi-objective training framework with parameter transfer for training. Based on theoretical modeling, we can find the best tradeoff between the two objectives. Moreover, to improve the model's generalization for various traffic flows, we use a GNN model to generate the rewards of the DRL model, which greatly speeds up the training phase and allows us to feed massive amounts of data into the model. Through evaluations of real-world network topologies, our approach shows a 10%-20% improvement in optimizing MLU under short traffic-ch
Advanced metering infrastructure (AMI) plays a key role in power systems. Since smart meters and meter collectors are synchronised to the time synchronisation devices (TSDs) in the head end system (HES) of AMI, they a...
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Advanced metering infrastructure (AMI) plays a key role in power systems. Since smart meters and meter collectors are synchronised to the time synchronisation devices (TSDs) in the head end system (HES) of AMI, they are vulnerable to global positioning system (GPS) spoofing-based time synchronisation attack (TSA). Impacts of GPS spoofing-based TSA on AMI are investigated in this study. It is uncovered that, since AMI is a distributed networked system and metering data and control commands transmitted in AMI could be of large latency, data and commands with large latency over the specific threshold are considered to be invalid according to validity verification mechanism of average distributedsystem. Therefore, the disorder in time synchronisation induced by GPS spoofing-based TSA could disable functions of HES of AMI, such as meter reading and remote control. A time jitter detection-based approach is developed to identify and prevent from GPS spoofing-based TSA. A high-precision oven-controlled crystal oscillator with cumulative error compensation is utilised to identify time jitter of the satellite clock and help ride through sustained GPS spoofing-based TSA. Simulation on FPGA demonstrates the effectiveness of the proposed approach.
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