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检索条件"任意字段=Proceedings of the 22nd International Conference on Distributed Computing and Networking"

共 1640 条记录，以下是151-160 订阅

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One-shot Bottleneck Size Optimization in CNN for Split computing

One-shot Bottleneck Size Optimization in CNN for Split Compu...

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Consumer Communications and networking conference, CCNC IEEE

作者： Yutaro Horikawa Takayuki Nishio School of Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Split computing (SC) is a distributed inference method designed to balance computational load and reduce latency by partitioning a neural network into head and tail networks, deployed on a mobile device and a server, respectively. Due to the limited bandwidth, the data size at the split point must be reduced. For this reason, layers with small channel sizes, called bottlenecks, are often introduced in the model. However, the degree to which the channel size should be reduced depends on the overall structure of the model and the difficulty of the task, so a search is necessary. In this paper, we propose a training method that simultaneously reduces the number of bottleneck channels and optimizes model parameters for SC. This method introduces weights for the bottleneck layer into the loss function of training using cross-entropy. This makes the output value of each channel in the bottleneck layer closer to zero, and reduces the size of the transmitted data by filling in the values close to zero at the server side instead of sending them by the client during inference.

关键词： Training Accuracy Neural networks Tail Machine learning Mobile handsets Filling Computational efficiency Servers Optimization

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An Efficient Graph Accelerator with distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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computing Power Network: An Interworking Architecture of computing and Network Based on IP Extension 22

Computing Power Network: An Interworking Architecture of Com...

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IEEE 22nd international conference on High Performance Switching and Routing (IEEE HPSR)

作者： Lei, Bo Zhao, Qianying Mei, Jie China Telecom Res Inst Beijing Peoples R China Beijing Univ Post & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665440059

The introduction of edge computing has brought new changes in network traffic model. From the perspective of users, resources in different locations are not equal. For providing users with a better service experience, the distance between users and resources (different latency), network conditions, as well as charging and many other factors are needed to be considered. This paper considering various resources proposes an interworking architecture of computing and network based on IP extension: computing power network. It is a new type of network that realizes the best resource allocation, by distributing computing, storage, algorithm and other resource information of service nodes through network control plane (such as centralized controller, distributed routing protocol, etc.). It combines network context and user requirements to provide the optimal distribution, association, transaction and scheduling of computation storage and network resources.

关键词： computing power network edge computing

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2022 IEEE 5th international conference on Hot Information-Centric networking, HotICN 2022

2022 IEEE 5th International Conference on Hot Information-Ce...

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5th IEEE international conference on Hot Information-Centric networking, HotICN 2022

ISBN: (纸本)9798350335200

The proceedings contain 22 papers. The topics discussed include: a publish-subscribe content synchronization scheme based on multicast tree in ndN;a systematic analysis to improve versatility of VERSEC trust schema;a named in-network computing service deployment scheme for ndN-enabled software router;access control supported by information service entity in named data networking;blockchain-based smart kitchen platform for the catering industry;research on architecture and application of computing network convergence service orchestration;self-healing motif-based distributed routing algorithm for mega-constellation;using federated learning to predict vulnerability exploitability;SRSV: efficient resource reservation for satellite networks based on segment routing;improved convergence and low retransmission of BBR congestion control algorithm based on adaptive pacing gain;and a scalable heuristic time-sensitive traffic scheduling algorithm for in-vehicle network.

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Sampling and Output Estimation in distributed Algorithms and LCAs 21

Sampling and Output Estimation in Distributed Algorithms and...

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22nd international conference on distributed computing and networking (ICDCN)

作者： Barenboim, Leonid Maimon, Tzali Open Univ Israel Raanana Israel Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781450389334

We consider the distributed message-passing model and the Local Computational Algorithms (LCA) model. In both models a network is represented by an n-vertex graph G = (V, E). We focus on labeling problems, such as vertex-coloring, edge-coloring, maximal independent set (MIS) and maximal matching. In the distributed model the vertices of v perform computations in parallel, in order to compute their parts in the solution for G. In the LCA model, on the other hand, probes are performed on certain vertices in order to compute their labels in a solution to a given problem. We study the possibility of estimating a solution produced by an algorithm, much before the algorithm terminates. This estimation not only allows for size estimation of a solution, but also for an early detection of failure in randomized algorithms, so that a correcting procedure can be executed. To this end, we propose a sampling technique, in which the labels in the sampling are distributed proportionally to the distribution in the algorithm's output. However, the sampling running time is significantly smaller than that of the algorithm in hand. We achieve the following results, in terms of the maximum degree Delta and the arboricity a of the input graph. The running time of our procedures is O(log a + log log n), for sampling vertex-coloring, edge-coloring, maximal matching and MIS. This significantly improves upon previous sampling techniques, which incur additional dependency on the maximum degree Delta that can be much higher than the arboricity, as well as more significant dependency on n. Our techniques for sampling in the distributed model provide a powerful and general tool for estimation in the LCA model. In this setting the goal is estimating the size of a solution to a given problem, by making as few vertex probes as possible. For the above-mentioned problems, we achieve estimations with probe complexity d(O(log a+log log n)), where d = min(Delta, a . poly(log(n)).

关键词： distributed Algorithms Randomized Scheme Graph Coloring Maximal Independent Set LCA

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Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case 1

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22nd international Semantic Web conference (ISWC)

作者： Zhou, Baifan Nikolov, Nikolay Zheng, Zhuoxun Luo, Xianghui Savkovic, Ognjen Roman, Dumitru Soylu, Ahmet Kharlamov, Evgeny Univ Oslo Dept Informat Oslo Norway Oslo Metropolitan Univ Dept Comp Sci Oslo Norway SINTEF AS Trondheim Norway Bosch Ctr Artificial Intelligence Renningen Germany ACM Member Berlin Germany Free Univ Bozen Bolzano Dept Comp Sci Bozen Bolzano Italy

ISBN: (数字)9783031472435

ISBN: (纸本)9783031472428;9783031472435

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.

关键词： ontology engineering knowledge graph semantic ETL machine learning cloud computing welding quality monitoring Industry 4.0 rule-based reasoning Datalog

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TUNE-FL: Adaptive Semi-Synchronous Semi-Decentralized Federated Learning

TUNE-FL: Adaptive Semi-Synchronous Semi-Decentralized Federa...

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Consumer Communications and networking conference, CCNC IEEE

作者： Houssem Jmal Kandaraj Piamrat Ons Aouedi École Centrale Nantes IMT Atlantique CNRS INRIA LS2N UMR Nantes University Nantes France SnT SIGCOM University of Luxembourg Luxembourg

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Today, Federated Learning (FL) stands out as the solution to addressing the challenges of distributed computing and empowering a wide range of edge devices with artificial intelligence capabilities. One variant of FL called semi-decentralized FL (SDFL) enables multiple server units to coordinate the learning task instead of relying on only one central server, hence preventing single-point failures. However, SDFL requires careful consideration regarding the coordination between server nodes, and dealing with the heterogeneous computing resources and data distributions across end devices (FL clients). Therefore, we propose TUNE-FL, an adapTive semi-synchronoUs semi-deceNtralizEd Federated Learning that addresses the clients' heterogeneity challenges. TUNE-FL alleviates these challenges by (i) ensuring consensus regardless of the network topology, and (ii) deploying an adaptive semi-synchronous mechanism for coordinating the learning process across all nodes while taking into consideration the heterogeneity presented by end devices. We evaluated TUNE-FL for the intrusion detection system (IDS) datasets and compared it with the three most representative baseline models. The experimental results demonstrate that TUNE-FL outperforms the baselines in accuracy while greatly reducing the duration of FL training by approximately 97 times.

关键词： Training Adaptive systems Federated learning Network topology Image edge detection Intrusion detection Heterogeneous networks Servers Synchronization distributed computing

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distributed and Parallel Low-Diameter Decompositions for Arbitrary and Restricted Graphs 16

Distributed and Parallel Low-Diameter Decompositions for Arb...

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16th Innovations in Theoretical Computer Science conference, ITCS 2025

作者： Dou, Jinfeng Götte, Thorsten Hillebrandt, Henning Scheideler, Christian Werthmann, Julian Paderborn University Germany University of Hamburg Germany

ISBN: (纸本)9783959773614

We consider the distributed and parallel construction of low-diameter decompositions with strong diameter. We present algorithms for arbitrary undirected, weighted graphs and also for undirected, weighted graphs that can be separated through k ∈ Õ(1) shortest paths. This class of graphs includes planar graphs, graphs of bounded treewidth, and graphs that exclude a fixed minor Kr. Our algorithms work in the PRAM, CONGEST, and the novel HYBRID communication model and are competitive in all relevant parameters. Given D > 0, our low-diameter decomposition algorithm divides the graph into connected clusters of strong diameter D. For an arbitrary graph, an edge e ∈ E of length e is cut between two clusters with probability O(e·log(n)D). If the graph can be separated by k ∈ Õ(1) paths, the probability improves to O(e·log log nd). In either case, the decompositions can be computed in Õ(1)1 depth and Õ(m) work in the PRAM and Õ(1) time in the HYBRID model. In CONGEST, the runtimes are Õ(HD + √n) and Õ(HD) respectively. All these results hold w.h.p.2 Broadly speaking, we present distributed and parallel implementations of sequential divide-and-conquer algorithms where we replace exact shortest paths with approximate shortest paths. In contrast to exact paths, these can be efficiently computed in the distributed and parallel setting [STOC’22]. Further, and perhaps more importantly, we show that instead of explicitly computing vertex-separators to enable efficient parallelization of these algorithms, it suffices to sample a few random paths of bounded length and the nodes close to them. Thereby, we do not require complex embeddings whose implementation is unknown in the distributed and parallel setting. © Jinfeng Dou, Thorsten Götte, Henning Hillebrandt, Christian Scheideler, and Julian Werthmann.

关键词： Undirected graphs

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TWAFR-GRU: An Integrated Model for Real-time Charging Station Occupancy Prediction 19

TWAFR-GRU: An Integrated Model for Real-time Charging Statio...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Qiyang Liu, Sheng Qu, Haohao Zhu, Rui You, Linlin Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Systems Science Department Singapore

ISBN: (纸本)9798350346558

The fast-growing number of electric vehicles has resulted in a variety of charging-related issues, such as the shortage of public charging piles, the inconvenience of finding available charging piles, the additional traffic congestion caused by unnecessary cruising to search for available charging piles, etc. Since only constructing new charging piles may not significantly improve the charging efficiency, methods of predicting charging station occupancy are widely discussed as an effective means to address these issues. However, several challenges are encountered in training an efficient and effective model by utilizing distributed data, improving convergence speed, and enhancing model generalization ability. To address these issues, this paper proposes a novel mechanism, named TWAFR-GRU, which integrates Temporally Weighted Asynchronous Federated Learning (TWAFL) with Reptile and Gated Recurrent Unit (GRU). As shown by the holistic evaluation based on the charging station occupancy dataset, compared with other state-of-the-art baselines, TWAFR-GRU can 1) decrease MAE, RMSE and RAE by 19%, 15% and 17% separately and improve R2, by 67%;2) cut rounds for the model to converge by 75%;3) save training time by 44%;and 4) reduce 17% in forecasting error after the personalization of the initial model to serve a specific charging station. © 2022 IEEE.

关键词： Piles

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Heuristic Data Placement in distributed Hierarchical Storage Network Using Deep Reinforcement Learning

Heuristic Data Placement in Distributed Hierarchical Storage...

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Consumer Communications and networking conference, CCNC IEEE

作者： Ravi Kaler Durga Toshniwal Computer Science and Engineering Department Indian Institute of Technology Roorkee Uttarakhand India

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Intelligent data placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient data placement can lead to significant delays in data movement and increased service latency, impacting the overall performance of a distributed environment. It is a complex online decision-making problem due to its varying access patterns, dynamic network conditions, and ever-changing distributed environment, which traditional network optimization techniques fail to address efficiently. Reinforcement learning is best suited to address this problem as it learns from the environment dynamically and performs action. This paper presents a novel deep reinforcement learning (DRL) framework, soft actor-critic-2 (SAC-2), for heuristic data placement in the distributed network, leveraging the state-of-the-art SAC technique to improve efficiency, reduce latency, and subsequently minimize storage costs. We formulate the problem as a Markov Decision Process (MDP) model and incorporate a prioritized replay buffer for efficient learning. The heuristic data placement technique improves distributed hierarchical storage system efficiency compared to baseline strategies, as demonstrated by extensive experiments on Microsoft Research Cambridge (MSR) network traces.

关键词： Performance evaluation Cloud computing Costs Markov decision processes Decision making distributed databases Deep reinforcement learning Delays Time factors Optimization

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