A Service Customized Reliable Routing Mechanism Based on SRv6.

Peichen Li, Deyong Zhang,Xingwei Wang,Bo Yi,Min Huang

Lecture Notes in Computer Science（2022）

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Abstract

Reliable routing is a classic problem in the field of computer networks. After a network fault occurs, how to choose the recovery path directly determines the performance of network services. This paper introduces service customized techniques into reliable routing. By meeting customized traffic protection requirements, network service quality can be ensured after fault recovery. Topology Independent Loop-free Alternate (TI-LFA) supported by SRv6 is a new reliable routing technology. In this paper, an SRv6-based service customized reliable routing mechanism is designed for the single link failure in the case of P-Q space adjacency in TI-LFA. For traffic with QoS requirements, this paper uses fuzzy theory to make the optimal decision for SRv6 candidate protection schemes. Finally, three representative topologies are selected to build an experimental network supporting SRv6 based on ONOS, Mininet, and the programmable data plane. The results show that when responding to a network service customized request, the recovery path selected by the mechanism proposed in this paper is superior to the comparison mechanism of related QoS indicators.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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