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LDChain: A Lightweight and Scalable Blockchain System for Dynamic IoT Scenarios 20th

LDChain: A Lightweight and Scalable Blockchain System for ...

引用

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

作者： Wang, Jianrong Cao, Yuxuan Hu, Dengcheng Li, Qi Li, Sen Li, Xuewei Liu, Xiulong College of Intelligence and Computing Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China School of Future Technology Tianjin University Tianjin China

ISBN: (纸本)9789819628292

As a distributed ledger, blockchain inherently guarantees data consistency. However, when applied to the Internet of Things (IoT), existing solutions employing blockchain systems struggle to address the scalability demands posed by the dynamic mobility of heterogeneous devices. To this end, we propose Lightweight DAG Chain (LDChain), a lightweight and scalable blockchain system tailored for IoT scenarios. LDChain connects weak nodes and strong nodes through a hierarchical DAG consensus algorithm. For weak nodes, we introduce a transaction-based DAG consensus mechanism utilizing a snapshot synchronization strategy to cater to the dynamic nature of weak nodes and the scalability demands of the system. The integrated snapshot synchronization strategy, based on the second latest block transaction selection algorithm, reduces the storage and computational overhead for resource-restricted devices. For strong nodes, we devise a block-based DAG consensus mechanism and a repackaging strategy, which resolves transaction conflicts across regions by managing conflicting transactions through a priority queue. We implement a prototype of LDChain and conduct experiments in a network environment comprising 10 cloud servers, simulating several strong nodes and weak nodes. The experimental results show that our system achieves a throughput exceeding 7000 transactions per second (TPS) and a transaction confirmation latency of 1.3 s with 100 nodes. © ifip international Federation for Information Processing 2025.

关键词： Scalability

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2FA Sketch: Two-Factor Armor Sketch for Accurate and Efficient Heavy Hitter Detection in Data Streams 20th

2FA Sketch: Two-Factor Armor Sketch for Accurate and Efficie...

引用

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

作者： Liu, Xilai Zhang, Xinyi Liu, Bingqing Li, Tao Yang, Tong Xie, Gaogang Institute of Computing Technologies Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China CNIC Chinese Academy of Sciences Beijing China Beijing University of Posts and Telecommunications Beijing China National University of Defense Technology Changsha China Peking University Beijing China

ISBN: (纸本)9789819628636

Detecting heavy hitters, which are flows exceeding a specified threshold, is crucial for network measurement, but it faces challenges due to increasing throughput and memory constraints. Existing sketch-based solutions, particularly those using Comparative Counter Voting, have limitations in efficiently identifying heavy hitters. This paper introduces the Two-Factor Armor (2FA) Sketch, a novel data structure designed to enhance heavy hitter detection in data streams. 2FA Sketch implements dual-layer protection through an improved Arbitration strategy for in-bucket competition and a cross-bucket conflict Avoidance hashing scheme. By theoretically deriving an optimal λ parameter and redesigning votenew+ as a conflict indicator, it optimizes the Comparative Counter Voting strategy. Experimental results show that 2FA Sketch outperforms the standard Elastic Sketch, reducing error rates by 2.5 to 19.7 times and increasing processing speed by 1.03 times. © ifip international Federation for Information Processing 2025.

关键词： Data streams

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RV-CVP: A Flexible Variable Precision RISC-V ISA Extension for Convolutional Neural network 20th

RV-CVP: A Flexible Variable Precision RISC-V ISA Extension f...

引用

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

作者： Zhu, Jingyi Zhao, Di Zhang, Weifeng Jia, Zhijie Liu, Boran Chen, Guokai Alibaba Group Hangzhou China Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Peking University Beijing China

ISBN: (纸本)9789819628292

Convolutional neural network (CNN) performs well in several research fields and achieves high accuracy. With the development of CNNs, various hardware acceleration methods have been proposed. Instruction Set Architecture (ISA) based acceleration methods are gaining attention by providing flexibility and efficiency. Meanwhile, for mobile or embedded environments, there is a preference for variable precision quantization data to meet different accuracy or performance requirements. In this paper, we design a new RISC-V ISA extension, named RV-CVP, which supports variable precision specifically for CNNs. It has 3 computational instructions for CNNs and 3 memory access instructions that closely match the computation. We implement the RV-CVP extension on the open-source processor NutShell. Then a CNN programming library is developed, which allows flexible use of the RV-CVP instructions and building CNN models. Finally, we implement our design in simulation and FPGA platforms and evaluate the code density, performance, hardware resource usage, and power consumption. The results show that the RV-CVP extension improves instruction density by up to 1.678x and program performance by up to 42.06x at the single operator level. While on real CNN models, the RV-CVP extension improves performance by 3.8x–4.4x. The code is available at https://***/OpenBPU/RV-CVP. © ifip international Federation for Information Processing 2025.

关键词： Convolutional neural networks

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DTuner: A Construction-Based Optimization Method for Dynamic Tensor Operators Accelerating 20th

DTuner: A Construction-Based Optimization Method for Dynam...

引用

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

作者： Chen, Wenxin Diao, Boyu Liu, Hangda Wang, RuiSheng Xu, Yongjun Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819628292

Auto-optimization of tensor programs is a crucial technique in deep learning compilers. Traditional tensor program optimization methods rely on the known fixed shape of the tensor, but when inputs of tensor program are only determined at runtime, these traditional methods cannot optimize in advance. To overcome this challenge, we introduce DTuner, a dynamic shape tensor program optimization method based on a constructive algorithmic. By optimizing for a broad variety of potential input forms, DTuner leverages highly focused and quick optimization methods to increase the efficiency of dynamic shape tensor programs, resulting in notable gains in average performance. Specifically, modern advanced GPUs are composed of general-purpose computing cores and specialized computing cores. Since different input shapes can yield varying performance outcomes across different cores, we incorporate a sampling and prediction method in the optimizer to guide the selection of computing cores. Evaluation results demonstrate that our method is 2 to 3 orders of magnitude faster in tuning time compared to the existing state-of-the-art dynamic tensor program optimization methods (Dietcode). Additionally, the optimization performance of the algorithm is comparable to that of state-of-the-art methods. On the Nvidia A100 and Orin platforms, DTuner can enhance the inference performance of commonly used NLP models by up to 27.5%, with average performance improvements of 6% to 7% for dynamic inputs. For typical single dynamic tensor programs, our method can also achieve a maximum performance improvement of 25.7% and an average improvement of 9%. © ifip international Federation for Information Processing 2025.

关键词： Tensors

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Gradient-Aware Incremental network Quantization 20th

Gradient-Aware Incremental Network Quantization

引用

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

作者： Meng, Jiao Qu, Zhihao Zhou, Wenxuan Hu, Shihong Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819628636

Quantization is an effective strategy for model compression, facilitating the efficient deployment of deep neural networks on resource-constrained edge devices. However, existing post-training quantization methods often fail to differentiate the significance of weights in the quantization process, which can lead to considerable information loss and adversely affect the model’s inference capability. To address this, We propose a Gradient-aware Incremental network Quantization method (GaINQ) that utilizes weight gradients to evaluate parameter significance, and accordingly guides the quantization process to achieve dynamic quantization of model parameters. Additionally, we incrementally apply quantization and integrate a nearest power-of-two approach to further compress the bit width required for weight representation. This strategy enhances storage efficiency and replaces complex arithmetic operations with shift operations during inference, thereby improving computational efficiency. When applied to the GoogLeNet model, GaINQ demonstrates a notable improvement over state-of-the-art quantization methods, with accuracy gains ranging from 0.6% to 4.39%, achieving an accuracy of 96.25%. © ifip international Federation for Information Processing 2025.

关键词： Deep neural networks

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Relocation of Container-Based Services in a MEC-NFV Orchestrated Environment 7th

Relocation of Container-Based Services in a MEC-NFV Orchestr...

引用

7th ifip WG 5.5 international Cross-Domain conference on Internet of Things

作者： Bernad, Cristina Kjorveziroski, Vojdan Roig, Pedro Alcaraz, Salvador Gilly, Katja Filiposka, Sonja Miguel Hernandez Univ Dept Comp Engn Elche Spain Ss Cyril & Methodius Univ Fac Comp Sci & Engn Skopje North Macedonia

ISBN: (纸本)9783031819025;9783031819001;9783031818998

With the rapid growth of real-time next-generation mobile services, it has become necessary to work towards holistic orchestration of the benefits promised with edge computing based on bringing the computing infrastructure closer to the end user. While the concept of Multi-access Edge computing (MEC) integrated with network Function Virtualisation (NFV) is being standardised, there is still a lot of work to be done to orchestrate the relocation of edge applications integrated in 5G and beyond systems in a smooth and efficient manner. In this paper, we document the current status of the transparent relocation of edge services in an experimentally deployed MEC-NFV environment based on OSM. Working towards gathering monitoring training datasets necessary for the development of proactive MEC application orchestrators that will implement seamless follow-me behaviour for MEC services, we provide benchmark results for the service downtime of three potential MEC services hosted in lightweight containers. Our analysis of results shows that containers exhibit improved performance over that of virtual machines, but there are still some issues that require improvement in both the orchestration implementation as well at the relocation process for containers.

关键词： Edge computing Optimisation Standardisation Migration Mobile nodes Containers

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Enhancing trusted computing operation efficiency based on parallel file verification techniques 4

Enhancing trusted computing operation efficiency based on pa...

引用

4th international conference on network Communication and Information Security, ICNCIS 2024

作者： Zhang, Zhilong Pan, Fengyan Hou, Haibo Li, Yuan Shanghai Jiaotong University Shanghai China Beijing Chenguang Rongxin Technology Co. Beijing China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

Trusted computing technology represents a significant element of cyber security systems, serving to guarantee the integrity and accessibility of data and systems. The incorporation of Trusted computing introduces a series of security detection and verification mechanisms to the host system, consequently impacting the performance of host system startup and application operation. In this paper, following an analysis of the verification mechanisms of Trusted computing, a parallel verification mechanism is proposed. The objective is to facilitate the parallel operation of the host system program and the TPM-based file verification process, thereby enhancing the operational efficiency of the trusted computing system. Through the configurable and micro-intervention design scheme, flexible expansion and configuration within the existing Trusted computing technology framework can be achieved. © 2025 SPIE.

关键词： computing systems Design Computer security Process control Systems modeling Mathematical optimization Operating systems

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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...

引用

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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Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster 20th

Efficient Implementation of the LOBPCG Algorithm on a CP...

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

作者： Liu, Yang Zhao, Yonghua Wang, Zexin Huang, Rongfeng Zhang, Dingye Zhang, Xinyin Computer Network Information Center Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Technology Shijiazhuang Tiedao University Hebei Shijiazhuang China

ISBN: (纸本)9789819628292

The Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) algorithm is an effective algorithm for solving large-scale sparse eigenvalue problems in various scientific and engineering applications. Based on a distributed CPU-GPU heterogeneous environment, this paper presents an optimized approach for the efficient solution of sparse eigenvalue problems utilizing the LOBPCG algorithm. To conceal intra-node data transfer and reduce the influence of the cross-node MPI communication overhead, we employ a sparse matrix regular two-dimensional partitioning scheme. To enhance GPU data throughput and data reuse, we consolidate multiple sparse matrix-vector multiplications (SPMV) into a single sparse matrix-matrix multiplication (SPMM). Furthermore, for the irregular dense matrix-matrix multiplication (GEMM) involved in the LOBPCG algorithm, we design an adaptive batched GEMM technique for GPU execution. Extensive experimental results demonstrate the computational efficiency and scalability of our approach. Our implementation achieves a significant speedup in solving typical matrices compared to the SLEPc library. The strong scalability performance exceeds 58%. © ifip international Federation for Information Processing 2025.

关键词： Scalability

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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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