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CCRPS: Customized cross-domain routing with privacy preservation and stable quality-of-experience based on deep reinforcement learning

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Information Sciences 2025年 716卷

作者： Zhang, Jialei Yan, Zheng Wang, Haiguang Li, Tieyan School of Cyber Engineering Xidian University Shaanxi Xi'an710126 China Hangzhou Institute of Technology Xidian University Hangzhou311231 China Huawei Technologies Co. Ltd. 9 North Buona Vista Dr 13-01 The Metropolis Tower 1 13858 Singapore

Next-generation networks are predominantly heterogeneous, integrating diverse network domains built on various technologies. The transmission of data with specific requirements across multiple network domains necessitates advanced cross-domain routing solutions. However, current approaches fall short in providing cross-domain customized routing that incorporates privacy protection and adapts to dynamic network conditions, often overlooking Quality of Experience (QoE) and its stability. To tackle these challenges, we propose CCRPS, a customized cross-domain routing scheme designed for Integrated Heterogeneous Networks (Inte-HetNet), which enables routing customization, supports dynamic network environments, ensures robust cross-domain privacy protection, and delivers consistent and efficient QoE. Specifically, CCRPS begins by formalizing user customization requirements to facilitate routing customization. Next, it reformulates the cross-domain routing generation problem as a multi-agent Deep Reinforcement Learning (DRL) task and develops a Customized Cross-domain routing algorithm based on Multi-agent DRL (CCR-MD) to address it, ensuring adaptability to dynamic network conditions. Additionally, CCRPS incorporates privacy protection mechanisms, such as virtual topology construction, node attribute calculation, and random obfuscation, to safeguard privacy during cross-domain routing. Moreover, it introduces a QoE-centric reward function to maintain QoE stability. Extensive experimental evaluations demonstrate the superior performance of CCRPS through comparison with existing related schemes. © 2025 Elsevier Inc.

关键词： dynamic routing algorithms

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Universal Model routing for Efficient LLM Inference

arXiv

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arXiv 2025年

作者： Jitkrittum, Wittawat Narasimhan, Harikrishna Rawat, Ankit Singh Juneja, Jeevesh Wang, Zifeng Lee, Chen-Yu Shenoy, Pradeep Panigrahy, Rina Menon, Aditya Krishna Kumar, Sanjiv Google United States

Large language models’ significant advances in capabilities are accompanied by significant increases in inference costs. Model routing is a simple technique for reducing inference cost, wherein one maintains a pool of candidate LLMs, and learns to route each prompt to the smallest feasible LLM. Existing works focus on learning a router for a fixed pool of LLMs. In this paper, we consider the problem of dynamic routing, where new, previously unobserved LLMs are available at test time. We propose a new approach to this problem that relies on representing each LLM as a feature vector, derived based on predictions on a set of representative prompts. Based on this, we detail two effective strategies, relying on cluster-based routing and a learned cluster map respectively. We prove that these strategies are estimates of a theoretically optimal routing rule, and provide an excess risk bound to quantify their errors. Experiments on a range of public benchmarks show the effectiveness of the proposed strategies in routing amongst more than 30 unseen LLMs. © 2025, CC BY.

关键词： dynamic routing algorithms

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SmarterROUTES-A Data-driven Context-aware Solution for Personalized dynamic routing and Navigation

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ACM TRANSACTIONS ON SPATIAL algorithms AND SYSTEMS 2021年第1期7卷 1–25页

作者： De Bock, Jelle Verstockt, Steven Univ Ghent IDLab Imec Technol Pk Zwijnaarde 122 B-9052 Zwijnaarde Belgium

SmarterROUTES contributes to personalised routing and navigation by data-driven route ranking and an environmentally aware road scene complexity-estimation mechanism. Traditional routing algorithms provide the fastest, shortest, or most ecological route by calculating the lowest-cost path from A to B using an underlying network of weighted connections. Our implementation goes a step further and ranks a set of such routes. The ranking is the result of additional weighing based on governmental data sets, extracts of the OpenStreetMap (OSM) database, or periodically adapted extracts of web services (e.g., Representational state transfer APIs). The selected data sets and their relative contributions to the overall ranking mechanism can be dynamically adapted by the end-user. Another major contribution toward a fully personalised navigation experience is the implementation of a road scene complexity scoring mechanism. Road complexity is estimated based on the combined input from geospatial data sources, traffic data, sensor analysis, and Street View-based complexity analysis. The latter input source uses Street Viewimages as input for a Densenet Convolutional Neural Network (CNN), pre-trained on buildings using Bag-of-Words and Structure-from-motion features, outputting an image descriptor. The current version uses an adapted version of this network to predict road complexity scores. The predicted values correspond with the subjective complexity judgements of the end-users.

关键词： Data mining machine learning algorithms dynamic routing algorithms scalable routing engines trajectory analysis (dealing with quality and uncertainty) web and real-time applications

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Harder Task Needs More Experts: dynamic routing in MoE Models 62

Harder Task Needs More Experts: Dynamic Routing in MoE Model...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Huang, Quzhe An, Zhenwei Zhuang, Nan Tao, Mingxu Zhang, Chen Jin, Yang Xu, Kun Chen, Liwei Huang, Songfang Feng, Yansong Peking University China Futurise AI Kuaishou Technology China

ISBN: (纸本)9798891760943

In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty. Unlike existing MoE approaches that rely on fixed TopK routing, which activates a predetermined number of experts regardless of the input's complexity, our method dynamically allocates experts based on the confidence level in expert selection for each input. This allows for more efficient utilization of computational resources, activating more experts for complex tasks requiring advanced reasoning and fewer for simpler tasks. Through extensive evaluations, our dynamic routing method demonstrates substantial improvements over Top2 routing across various benchmarks, achieving an average improvement of 0.7% with less than 90% activated parameters. Further analysis shows our model dispatches more experts to tasks requiring complex reasoning skills, like BBH, confirming its ability to dynamically allocate computational resources in alignment with the input's complexity. Our findings also highlight a variation in the number of experts needed across different layers of the transformer model, offering insights into the potential for designing heterogeneous MoE frameworks. The code and models are available at https://***/ZhenweiAn/dynamic_MoE. © 2024 Association for Computational Linguistics.

关键词： dynamic routing algorithms

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dynamic routing Based on Satellite Reliability Evaluation in Satellite Optical Networks

Dynamic Routing Based on Satellite Reliability Evaluation in...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Sun, Zhichun Xi, Chao Hou, Xiaojie Li, Haicheng Yang, Bo Zhu, Ruijie Zhengzhou University Zhengzhou China Space Star Technology Co. Ltd. Beijing China Zhengzhou University Songshan Laboratory Zhengzhou China

A dynamic routing algorithm based on satellite reliability evaluation model is proposed to reduce the impact of satellite failure in satellite optical networks. Simulations show that our algorithm can achieve lower tr... 详细信息

ISBN: (纸本)9798350379266

关键词： dynamic routing algorithms

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Multi-Constraint Contraction Hierarchies with dynamic Label Restrictions for routing in Optical Networks

Multi-Constraint Contraction Hierarchies with Dynamic Label ...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Pan, Yan Fan, Yingbo Guo, Jiaxing Zhou, Bingrong Li, Yajie Zhao, Yongli Zhang, Jie Beijing University of Posts and Telecommunications Beijing100876 China

We propose a multi-constraint routing algorithm with dynamic label restrictions based on the contraction hierarchies, considering spectrum, OSNR, and delay. Simulation shows the algorithm finds a path within 0.2ms at ... 详细信息

ISBN: (纸本)9798350379266

关键词： dynamic routing algorithms

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Redistribution Layer routing with dynamic Via Insertion Under Irregular Via Structures 24

Redistribution Layer Routing with Dynamic Via Insertion Unde...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Chuang, Je-Wei Wu, Zong-Han Huang, Bo-Ying Chang, Yao-Wen Graduate Institute of Electronics Engineering National Taiwan University Taipei106319 Taiwan Department of Electrical Engineering National Taiwan University Taipei106319 Taiwan

ISBN: (纸本)9798400706011

In modern advanced packaging, redistribution layers (RDLs) are often used for signal transmission among chips, and vias are used for communication among different layers. Most existing RDL routers perform via planning before routing. However, since vias can be placed at arbitrary locations under the irregular via structure, via planning limits the solution space and reduces layout flexibility. This paper proposes a new flow with a novel routing graph model for 90- and 135-degree routing, which allows dynamic via insertion during routing. The proposed algorithm enlarges the solution space by providing more choices during path-finding, achieving higher routing quality. The experimental results based on commonly used benchmark suites show that our router achieves over 10% better wirelength with over 29X speedup over the state-of-the-art work and even achieves 0.4% better wirelength with 55X speedup over the state-of-the-art any-angle router. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： dynamic routing algorithms

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The Convergence of dynamic routing Between Capsules

The Convergence of Dynamic Routing Between Capsules

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2024 IEEE International Conference on Data, Information, Knowledge and Wisdom, DIKW 2024

作者： Ye, Daoyuan Li, Juntao Shen, Yiting School of Informatics Beijing Wuzi University Beijing China

ISBN: (纸本)9798331540548

Capsule networks(CapsNet) are recently proposed neural network models with new processing layers, specifically for entity representation and discovery of images. It is well known that CapsNet have some advantages over traditional neural networks, especially in generalization capability. At the same time, some studies report negative experimental results. The causes of this contradiction have not been thoroughly analyzed. The preliminary experimental results show that the behavior of routing algorithms does not always produce good results as expected, and in most cases, different routing algorithms do not change the classification results, but simply polarize the link strength, especially when they continue to repeat without stopping. To realize the true potential of the CapsNet, deep mathematical analysis of the routing algorithms is crucial. In CapsNet, each capsule uses the length of the pose vector to represent the probability of the presence of the entity represented by it. An irregular nonlinear function is required in the dynamic routing algorithm to keep the vector length less than 1. Based on this, Hinton et al. didn't believe the existence of any sensible objective function that is minimized by the dynamic routing algorithm. However, in this paper, we will give the objective function that is minimized by the dynamic routing algorithm, which is a concave function. The dynamic routing algorithm can be regarded as nonlinear gradient method to solving an optimization algorithm under linear constraints, and its convergence can be strictly proved mathematically. Furthermore, the mathematically rigorous proof of the convergence is given for this class of iterative routing procedures. We analyze the relation between the objective function and the constraints solved by the dynamic routing algorithm in detail, and perform the corresponding routing experiment to analyze the effect of our convergence proof. © 2024 IEEE.

关键词： dynamic routing algorithms

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The method of dynamic routing in Self-healing Networks 5

The method of dynamic routing in Self-healing Networks

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Anna, Shyman Kuchuk, Nina Kovalenko, Andriy Kashkevich, Svitlana Partyka, Stanislav Yankovskyi, Oleksandr Department of Computer Engineering and Programming National Technical University Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Computerized Management Systems National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350384499

The report proposes a dynamic routing method for Self-healing Networks (ShN). The method takes into account the specific features of ShN. During routing, the flow of service information is reduced. The search for the optimal route is carried out using a convolutional neural network. The method significantly outperforms existing methods when some network nodes fail. ©2024 IEEE.

关键词： dynamic routing algorithms

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dynamic routing Optimization Method for UAV Swarm Networks: An Evolutionary Game Approach

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年

作者： Wu, Di Jin, Chenlang Yao, Haipeng Mai, Tianle Xin, Xiangjun Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314019 China BUPT State Key Laboratory of Information Photonics and Optical Communications Beijing100876 China

With the ongoing advancement of information and communication technologies, the communication technologies for UAV swarm networks have undergone rapid development, especially in the context of large-scale UAV network deployments. In recent years, UAVs have found wide-ranging applications in both military and civilian domains. However, the inherent complexity and high dynamic nature of UAV swarm activities present substantial challenges to traditional routing algorithms, prompting the need for the design and implementation of efficient and sustainable routing solutions. To address these challenges, this paper introduces a UAV swarm routing algorithm based on evolutionary game theory, with a particular focus on energy efficiency and resource optimization. We leverage evolutionary game theory to enhance cooperation among nodes and adopt a strategy update rule that imitates the best-performing agents. In the proposed algorithm, nodes engage in continuous packet forwarding and participate in game interactions with neighboring nodes, adjusting their strategies based on accumulated gains. This strategy not only significantly enhances network lifetime and improves the packet delivery rate but also optimizes energy consumption and resource utilization, aligning with sustainable computing principles. To validate the effectiveness of the proposed routing method, we conduct extensive simulation experiments within a designed and implemented system model under different environmental contexts. The analysis confirmed the accuracy and effectiveness of the proposed routing method, highlighting its exceptional performance in terms of the network survival time, the number of successfully transmitted packets, and the adaptability in dynamic scenarios. © 2016 IEEE.

关键词： dynamic routing algorithms

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