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Intelligent Telemetry: P4-Driven network Telemetry and Service Flow Intelligent Aviation Platform 20th

Intelligent Telemetry: P4-Driven Network Telemetry and Serv...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Gao, Jing Zhou, Fanqin Dong, Mianxiong Feng, Lei Ota, Kaoru Li, Zijian Fan, Jiawei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Information and Electronic Engineering Muroran Institute of Technology Muroran050-8585 Japan Beijing University of Posts and Telecommunications Address Beijing Beijing100876 China

ISBN: (纸本)9789819628292

To address the current challenges in network monitoring and management, such as poor real-time performance, high cost, low accuracy, and slow fault detection and recovery, a P4-driven network telemetry and resource scheduling platform is designed and presented in this paper. This platform embeds telemetry information within data packets, achieving real-time monitoring and anomaly detection of network states. It significantly enhances the granularity and timeliness of network monitoring and employs a path rerouting mechanism for rapid network recovery. The platform boasts high scalability and customizability, allowing on-demand adjustment of telemetry strategies and parameters. It provides low-overhead, business-level telemetry information, offering comprehensive support for diverse services and advancing network management and service quality. This work provides a technical foundation for constructing the next generation of intelligent, efficient, and reliable networks through continuous research and practical application. © ifip international Federation for Information Processing 2025.

关键词： Telemetering

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ADMM for Energy-Efficient Computation Offloading in Marine Mobile Edge computing networks 20th

ADMM for Energy-Efficient Computation Offloading in Marine...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： He, Ang Lu, Zili Qin, Baolin Pan, Heng Si, Xueming Dai, Yueyue Huang, Xiaoyan Zhang, Yan Frontier Information Technology Research Institute Zhongyuan University of Technology Zhengzhou China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of Informatics University of Oslo Oslo Norway

ISBN: (纸本)9789819628636

Marine mobile edge computing (MMEC) networks play a crucial role in collecting and analyzing oceanic data. However, the limited energy storage capacity and inconvenient power delivery of underwater sensor nodes (USNs) and surface sink nodes (SSNs) necessitate energy-saving operations. Moreover, the exhaustion of energy in individual USN results in a diminished lifespan of the underwater sensor networks, thereby significantly affecting the accuracy and reliability of oceanic monitoring data. In this paper, we propose a computation offloading scheme that utilizes collaborative underwater sensor data transmission via acoustic and radio frequency links. The main objective is to minimize the energy consumption of unmanned aerial vehicle (UAV)-aided MMEC networks through the joint optimization of USN clustering, SSN computation offloading decisions, and UAV trajectory planning. To address this joint problem, we design a new clustering algorithm followed by the alternating direction method of multipliers (ADMM) optimization algorithm sequentially. Numerical results show that, compared to no clustering and no computation offloading methods, the total energy consumption for the proposed method is reduced by 23.3% and 10.1%, respectively. © ifip international Federation for Information Processing 2025.

关键词： Energy efficiency

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MTEE: Multiscale Temporal Entropy Evaluation Paradigm for Heterogeneous Complex Datasets 20th

MTEE: Multiscale Temporal Entropy Evaluation Paradigm for H...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： An, Ledong Wang, Chenyang Huang, Shaoyuan Guo, Yang Zhang, Cheng Qiu, Chao Wang, Xiaofei College of Intelligent and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China Tianjin University of Finance and Economics Tianjin China Software Technology Company Limited Suzhou215163 China

ISBN: (纸本)9789819628292

The rapid advancements in time-series forecasting have led to the emergence of datasets with increasingly diverse characteristics. Researchers typically focus on designing robust algorithms to handle these datasets. However, model performance can vary across different datasets. Existing studies commonly use Mean Absolute Error (MAE) and Mean Squared Error (MSE) to evaluate model performance. These metrics often need to fully account for the impact of dataset quality on forecast accuracy and reliability, leading to insufficient explanations of model forecasts and even dataset dependence. Furthermore, the lack of dataset evaluation makes it difficult to determine whether forecast results are influenced by data characteristics or model architecture, significantly hindering model improvement. To address this problem, this study proposes a new dataset evaluation paradigm-Multiscale Temporal Entropy Evaluation Paradigm. This paradigm aims to tackle the problem of data feature ambiguity affecting model forecast interpretability. By evaluating nine time-series datasets from domains such as weather, economics, transportation, and networks and comparing these evaluations with the forecast results from six different models, this study demonstrates the effectiveness of this method in explaining the relationship between dataset features and model performance. © ifip international Federation for Information Processing 2025.

关键词： Spatio-temporal data

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A Large-Scale Study of Abnormal Recursive DNS 20th

A Large-Scale Study of Abnormal Recursive DNS

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Hu, Anlei Gong, Liangyi Xie, Jieling Li, Yufu Xie, Gaogang China Internet Network Information Center Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819628636

Recursive DNS plays a pivotal role in safeguarding the security and reliability of users’ access to diverse Internet services. A malfunctioning recursive DNS may impair users’ online experience and pose a grave threat to the security and integrity of the domain name system. Nevertheless, the huge scale and scattered distribution characteristics of recursive DNS make the measurement of abnormal recursive DNS very difficult. In this study, we will take an in-depth look at how top-level domain resolution logs can be used to evaluate and dissect large-scale recursive DNS query patterns. Precisely, we adopt a passive measurement approach, analyzing a massive dataset comprising 1.3 billion resolution logs gathered from. CN top-level domain name servers and also encompassing 110,599 source IPs. Leveraging our profound domain knowledge, we have identified and extracted 12 key features of aberrant recursive DNS query behaviors. Furthermore, we developed a t-SNE-based density clustering model, which has successfully pinpointed 1,417 anomalous recursive DNS servers. Finally, based on our domain expertise and authoritative experts’ insights, we classify these abnormal recursive DNS query behaviors into four categories. Through an in-depth analysis of the intrinsic driving mechanisms of four abnormal behaviors, we provide a comprehensive foundation for recursive DNS safety governance and thus enhance users’ online security. © ifip international Federation for Information Processing 2025.

关键词： network security

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AsymFB: Accelerating LLM Training Through Asymmetric Model parallelism 20th

AsymFB: Accelerating LLM Training Through Asymmetric Model P...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Zhang, Jiawei Shao, En Wang, Leping Tan, Guangming Sun, Ninghui Institution of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819628292

Transformer-based large language models (LLMs) have become a dominant force in natural language processing, advancing both research and industry. As model sizes have grown from billions to hundreds of billions of parameters, training them on a single GPU is no longer feasible, making distributed training essential. Existing distributed training methods partition models into non-overlapping segments of equal-size to cut memory usage on each device, but this creates cross-device data dependencies. These dependencies lead to delays and reduced throughput, particularly during the forward phase. This paper introduces AsymFB, a novel model parallelism algorithm designed to address these limitations. By adopting an asymmetric partitioning approach, AsymFB assigns varying numbers of devices to different training phases—forward, recompute, and backward—optimizing resource allocation and minimizing performance penalties. Our approach demonstrates up to a 1.25× speedup compared to traditional methods, significantly improving the efficiency of training large-scale LLMs and mitigating common performance bottlenecks. © ifip international Federation for Information Processing 2025.

关键词： Resource allocation

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SC-TSDRL: A Cloud-Edge Collaboration Framework for Diffusion Model Inference Acceleration 20th

SC-TSDRL: A Cloud-Edge Collaboration Framework for Diffusio...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Wang, Huiyu Liu, Zhicheng Wang, Xiaofei Qiu, Chao Zhang, Cheng Wang, Wenyu Ye, Qianwen College of Intelligent and Computing Tianjin University Tianjin China Tianjin University of Finance and Economics Tianjin China Shanghai Zhuichu Networking Technologies Co. Ltd. Shanghai China Hubei Central China Technology Development of Electric Power Co. Ltd. Wuhan China

ISBN: (纸本)9789819628636

In recent years, the rapid rise of Artificial Intelligence Generated Content (AIGC) has been accompanied by a surge in demand for generative AI application services. This trend has not only driven the diversified deployment of large-scale models but also posed more significant challenges to the efficient inference of these models. Current computing solutions have struggled to solely keep pace with growing user demands, which results in insufferable latency and inefficient performance. Integrating cloud computing and edge computing for the inference process of large models is deemed a promising solution, while related resource scheduling issues are still pending. To address this challenge, we focus our research on diffusion models, a representative model in the field of generative artificial intelligence, and propose a cloud-edge collaborative framework to accelerate the inference pipeline of text-to-image diffusion models. Our optimization goal is to minimize the average completion time of inference tasks while maintaining image quality. Considering that the inference for each image is an iterative process, we propose a step clipping strategy to save and reuse intermediate results to reduce iterations, compressing the calculation amount of inference tasks. Above this, we design a task scheduling strategy using proximal policy optimization, which can make efficient resource decisions. Experiments demonstrate the superiority of our strategy over local execution, greedy task scheduling strategy, and learning-based task scheduling strategies. Compared with the three baselines, the average task completion time under different system configurations is reduced by 1.093 s, 1.024 s, and 0.481 s, respectively. © ifip international Federation for Information Processing 2025.

关键词： Cloud platforms

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A Utility-Adjustable Reverse Auction Mechanism for UAV Data Collection Task Allocation 20th

A Utility-Adjustable Reverse Auction Mechanism for UAV Data...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Chen, Peng Zhang, Jixian School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9789819628636

Due to their low cost, easy deployment, and high flexibility, unmanned aerial vehicles(UAV) have become widely utilized for various data collection tasks across different fields. In particular, virtual service providers(VSP) recruit UAVs to collect data in their regions of interest(ROI), and leverage this data to offer a range of services, such as metaverse, traffic management, etc. To attract UAV owners to participate in the recruitment activities, VSP need to provide participating users with payments as rewards to motivate their participation. However, the recruitment demands of different VSPs for UAVs can vary significantly. For instance, time-sensitive tasks require timely recruitment of sufficient UAVs to ensure prompt completion of data collection tasks, necessitating higher payments to increase user enthusiasm. Conversely, in other types of tasks, the VSP’s objective might be to maximize its own utility. However, designing a general incentive mechanism for VSPs with different tasks that satisfies incentive compatibility(IC) and individual rationality(IR) to address the issues of UAV task allocation and payment is challenging. If VSP offer insufficient payments to users, it will be difficult to attract enough users to share their UAVs;conversely, offering excessive payments to users will harm the VSP’ profits. To address this problem, we propose a utility-adjustable reverse auction mechanism based on reverse Vickrey-Clarke-Groves(VCG) auction and affine maximizer auction(AMA) theory to cope with different task scenarios. This incentive mechanism can maximize social welfare while flexibly adjusting the utility allocation between VSP and users, ensuring non-negative utility for both parties. Experimental results show that the mechanism has excellent performance. © ifip international Federation for Information Processing 2025.

关键词： Unmanned aerial vehicles (UAV)

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DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies 20th

DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Frie...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Guo, Mingfeng Deng, Liang Dai, Zhe Li, Ruitian Lin, Gaofeng Liu, Jie Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819628292

Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm on GPU due to its short preprocessing time and outstanding performance. However, we observed that this algorithm still has two performance bottlenecks. Firstly, the thread-level parallel mode can introduce to thread divergence issues within GPU warps during the writing phase. Secondly, the thread-level and warp-level fusion mode may struggles to fully exploit GPU resources due to suboptimal mapping relationships between rows and threads. To address these issues, this paper proposes DaCPSpTRSV, a new synchronization-free algorithm with GPU-friendly data communication and parallelism strategies. Specifically, we first develop a fast-forward thread-level approach, incorporating an efficient global memory access pattern and a light-weight dependency control mechanism, to optimize data communication and alleviate thread divergence. A fine-grained fusion strategy is then proposed to maximize GPU parallelism by adaptively selecting the suitable thread-level or warp-level modes. Moreover, the commonly-used compressed sparse row (CSR) format is employed in our DaCPSpTRSV, enhancing the versatility of our algorithm. We evaluate our approach using 245 matrices from the SuiteSparse Matrix Collection on two NVIDIA GPUs, demonstrating speedup ratios of up to 4.77×, 4.94×, 1.67×, and 1.62× compared to cuSPARSE, Sync-Free, CapelliniSpTRSV, and YuenyeungSpTRSV, respectively. The project is open-sourced at https://***/gmfff12334/DaCP. © ifip international Federation for Information Processing 2025.

关键词： Graphics processing unit

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LBoDSN: An In-network Load Balancing Mechanism for Lossless Data Center networks Based on Direct Switch Notification 20th

LBoDSN: An In-Network Load Balancing Mechanism for Lossless...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Shi, Qingyu Jiang, Fangxue Huang, Huang Li, Xiaocui Li, Chuang Cao, Wenzhi Liu, Limei School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China

ISBN: (纸本)9789819628292

With the extensive deployment of Remote Direct Memory Access (RDMA) in lossless data center networks (DCNs), enhancing the RDMA load balancing performance for distributed AI training and HPC applications becomes particularly critical. However, existing load balancing schemes either suffer difficulty in responding to congestion in sub-RTT time, or cannot accurately detect and reroute flows that are on the verge of creating path congestion for Priority-based Flow Control (PFC) enabled lossless DCNs. In this paper, we propose LBoDSN, an in-network load balancing mechanism for lossless DCNs based on direct swtich notification, to address above challenges. LBoDSN monitors ingress queue length evolution at destination switches to predict the triggering time of PFC pause, and accurately identifies congested flows based on the congestion contribution level before PFC pause, then further proactively sends the flow congestion notification (FCN) to source switches for fast rerouting. The FCN contains a flow ID that identifies the congested flow, and a path ID that identifies the target path to which the congested flow will be rerouted. And after rerouting, the Congestion Notification Packet (CNP) of the old path is selectively discarded at source switches to improve transmission performance, while out-of-order packets are reordered at the destination switch. The experimental results show that under realistic workloads, LBoDSN achieves 9–64% and 20–80% better than CONGA for the average and tail Flow Completion Times (FCTs), respectively. Compared with ConWeave, LBoDSN achieves around 8% better performance for the average and tail FCTs while significantly reducing switch queue usage for reordering. © ifip international Federation for Information Processing 2025.

关键词： Time switches

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MiDedup: A Restore-Friendly Deduplication Method on Docker Image Storage Systems 20th

MiDedup: A Restore-Friendly Deduplication Method on Docker ...

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20th ifip WG 10.3 international conference on network and parallel computing, npc 2024

作者： Qin, Lisha Tan, Haoliang Zou, Xiangyu Ou, Wenhao Wei, Yan Huang, Rubing Xia, Wen Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Zhongruan International Technology Service Co. Ltd. Shenzhen China Macau University of Science and Technology Macao China

ISBN: (纸本)9789819628292

Docker image storage systems, like Docker registry, often employ deduplication to reduce storage overhead. Existing deduplication methods for these systems detect redundancy at either layer or file level, each with its own advantages and limitations. Layer-level deduplication offers much faster restoration but lower deduplication ratio, as it eliminates duplicate layers while ignoring duplicate files across layers. Conversely, file-level deduplication offers a higher deduplication ratio but slower restoration, as removing duplicate files scatters the files of a layer, leading to multiple disk read I/O during restoration. Our analysis reveals that the slow restoration in file-level deduplication partly stems from ignoring file-layer relationships. In this paper, we propose MiDedup, a new Docker deduplication method. MiDedup detects redundancy at file-level and organizes files into a restore-friendly layout by considering file-layer relationships to enhance restore speed. Our studies also show that duplicated files of one layer are not uniformly distributed across different layers. MiDedup uses this insight to skip deduplicating scattered files to further optimize the file layout for faster restoration. Evaluations indicate that MiDedup achieves restore speeds that are 8–19× faster than file-level deduplication (slightly slower than layer-level one), while maintaining 9–47% higher deduplication ratios than layer-level deduplication. © ifip international Federation for Information Processing 2025.

关键词： Redundancy

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