Mitigation of Phase Transitions in Self-Organizing NoC for Stable Queueing Dynamics

Most complex cooperative systems, such as networks on chip (NoCs), possess self-organizing properties and exhibit fluctuations in data traffic with similar statistical characteristics across multiple timescales, a.k.a., scaling behavior. Abrupt transitions in the scaling behavior of these fluctuations, caused by spikes in data traffic, network congestion, etc., indicate instability in the queueing dynamics of NoC routers. This instability hampers the predictability of real-time flow control mechanisms, leading to unpredictable delays and communication failures. Detecting and mitigating these instabilities or phase transitions is crucial in domains requiring stability and real-time control, such as aviation and healthcare. In this paper, we propose a real-time monitoring and characterization strategy for data traffic from influential routers to identify and mitigate impending instabilities before their onset. Leveraging the self-organization characteristic of NoCs, we propose to implement targeted mitigation on influential nodes to achieve network-wide effects. We demonstrate the effectiveness of our strategy on various benchmarks by comparing traffic analysis plots before and after mitigation. Our results show that the proposed phase transition mitigation improves the network performance by an average of 39.6% and buffer utilization by an average of 4.62%.