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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4330 条记录，以下是421-430 订阅

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On an Approximation Algorithm Combined with D3QN for HDFS Data Block Recovery in Heterogeneous Hadoop Clusters

On an Approximation Algorithm Combined with D3QN for HDFS Da...

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Intelligent Systems conference

作者： Zhang, Yijie Wu, Chase Q. Hou, Aiqin New Jersey Inst Technol Newark NJ 07102 USA Northwest Univ Xian 710127 Shaanxi Peoples R China

ISBN: (纸本)9783031663284;9783031663291

Hadoop stands as a cornerstone in the realm of big data processing, with its Hadoop distributed File System (HDFS) serving as a pivotal layer ensuring fault tolerance and high throughput data storage. Through mechanisms such as block replication and cluster-wide distribution, HDFS facilitates parallel computing in higher layers. However, the inherent heterogeneity within Hadoop clusters introduces complexities, particularly concerning the reliability of stored data. The failure of DataNodes within heterogeneous clusters poses a significant risk, potentially leading to data loss and compromising data reliability. Notably, the default block recovery strategy within HDFS overlooks the varying capacities of data nodes and the diverse patterns of data access, rendering it inadequate for heterogeneous environments. To address this gap, we first propose a novel approach for block recovery selection based on dueling double deep Q-networks, augmented with Gaussian Process Regression. We further formulate block recovery placement as an optimization problem in heterogeneous clusters, show its NP-completeness, and design an approximation algorithm that leverages linear programming-based iterative rounding (LPIR-BR), which offers a robust performance guarantee. Extensive experimental results validates the efficacy of LPIR-BR, showcasing its superiority over existing algorithms and affirming the soundness of our theoretical framework.

关键词： Big data Block distribution Approximation algorithm Dueling double deep Q-network Hadoop distributed file system Performance bound Reliability

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F2MH Cryptosystem: Preliminary analysis of an original attempt to revive Knapsack-based public-key encryption schemes 28

F2MH Cryptosystem: Preliminary analysis of an original attem...

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Villas Boas, Yuri da Silva Rocha, Daniel Santana de Barros, Charles F. Martina, Jean Everson Univ Fed Santa Catarina Inst Informat & Stat Florianopolis SC Brazil Inst Pure & Appl Math Rio De Janeiro Brazil Fed Univ Sdo Jodo Del Rey Dept Comp Sci Sao Joao Del Rei Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

Public-key cryptography is an ubiquitous building-block of modern telecommunication technology. Among the most historically important types, the knapsack-based encryption schemes, from the early years of public-key cryptography, performed particularly well in computational resources (time and memory), and mathematical and algorithmic simplicity. Although their widespread adoption was readily curtailed by effective cryptanalyses to several different attempts, the question of whether or not there is any future for actual usage of knapsack-based asymmetric encryption schemes, and all its potential advantages remains unsettled. The goal of this paper is to present a novel construction, which offer consistent security improvements on knapsack-based cryptography. We propose two improvements upon the original knapsack cryptosystem that address the most important types of attacks: the Diaphantine approximations-based attacks and the lattice problems oracle attacks. The proposed defenses demonstrably preclude the aforementioned types of attacks.

关键词： Knapsack Problem Subset Sum Problem Public-Key Cryptosystem Merkle-Hellman Cryptosystem Lattice Oracle Diophantine Approximation Shamir Cryptanalysis

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Encrypted Image Retrieval Scheme based on Neural network and LBP Mapping 29

Encrypted Image Retrieval Scheme Based on Neural Network and...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Lan Wang, Tian Wang, Zheng Jiangsu College of Finance and Accounting School of Ie Lianyungang222061 China

ISBN: (纸本)9798350330717

The privacy-preserving image retrieval research has been a hot issue for cyber-physical system security. Image encryption could effectively meet the growing privacy needs of users. However, the existing searchable encryption schemes are computationally complex, time-consuming, and have low retrieval accuracy. What's more, the image feature extraction requires multiple round communications between the cloud server and the image owner, which causes additional communication burdens. To solve these problems, a privacy-preserving image retrieval scheme combining deep learning technology and Local Binary Pattern (LBP) features is proposed. To protect images, block permutation, order-preserving encryption, and intra-block pixel substitution with multiple tables are used. The encrypted images would be uploaded to the cloud server and the LBP maps of encrypted images were calculated on the cloud side. Then, the neural network was trained by the encrypted LBP maps, and the image features were extracted from them with the trained neural network. The extracted features establish the image index. The Manhattan distance of features was utilized to measure the similarity between the stored images. Finally, the images similar to the query image were returned to the image owner. The security analysis and experimental results show that the proposed scheme is better than most existing schemes regarding security and retrieval accuracy. © 2023 IEEE.

关键词： Computation outsourcing cyber-physical system local binary pattern searchable encryption secure feature extraction

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Adaptive Overlap Padding and Resolution Selection for Frame Split-based Edge Video Analytics 29

Adaptive Overlap Padding and Resolution Selection for Frame ...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhu, Haopeng Huang, Zhaowu Guo, Xiaolin Liu, Mengyang Chen, Baijun Dong, Fang Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350330717

For providing accurate and fast on-device high-resolution video analytics, edge-assisted methods are widely proposed by using lower-resolution frames and splitting them with overlap padding. The use of low-resolution frames can significantly reduce the computational workload of video analytics. Dividing a frame into multiple overlapping parts simultaneously improves parallelism and maintains processing accuracy. However, accuracy and latency serve as a pair of tradeoff metrics, and prioritizing one to optimize overlap padding or resolution selection will compromise the other metric. Fortunately, we have discovered that in real-world video analytics scenarios with varying object sizes, it is not necessary to simultaneously achieve a high overlap padding size and high resolution. Hence, how to set appropriate overlap padding size and resolution to strike a balance between accuracy and latency in practical scenarios is a nontrivial and intractable problem. To this end, we propose an online learning-based method to achieve adaptive overlap padding and resolution selection, called APR. We model the problem as an integer programming and propose a Muli-armed bandit (MAB) theory-based algorithm to solve it. We discretize the continuum overlap padding size into a finite set to narrow explore space and set the frame split strategy as context information to achieve fast convergence. Theoretical analysis reveals APR achieves sub-linear regret. Extensive experimental results show APR outperforms the benchmark methods, achieving up to 2.06 × speedup in terms of latency and 0.18× increase in accuracy. © 2023 IEEE.

关键词： Deep learning Edge computing Object detection Video analytics

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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning

A New Pipeline for Knowledge Graph Reasoning Enhanced by Lar...

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2024 conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Chen, Zhongwu Bai, Long Li, Zixuan Huang, Zhen Jin, Xiaolong Dou, Yong National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete. Recent LLM-enhanced KGR models input KG structural information into LLMs. However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs. Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking. Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations. based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs. In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further. Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Using Programmable P4 Switches to Reduce Communication Costs of parallel and distributed Simulations

Using Programmable P4 Switches to Reduce Communication Costs...

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IEEE Global Communications conference (GLOBECOM)

作者： Wang, Shie-Yuan Kuo, Nai-En Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781665435406

In the past, several time synchronization methods have been proposed for parallel and distributed simulation (PDS). Among them, one widely used conservative method is the Chandy-Misra-Bryant (CMB) algorithm. In the CMB algorithm, many null messages may be exchanged among logical processes to advance their clocks so that deadlock will not occur among them. In this work, using a data-plane programmable P4 hardware switch, we design and implement a data fusion-based approach inside the packet processing pipeline of the P4 switch. Our approach extracts the timestamp carried in exchanged null messages, computes the fusion results of these timestamps, drops unnecessary null messages inside the switch, generates new messages carrying the fusion results, and sends these generated messages to only the logical processes that can benefit from receiving these messages. Experimental results show that on an 8-host testbed, our approach can speed up a PDS by a factor of 2.75 and 1.65 when compared with the unicast and multicast approaches, respectively.

关键词： software-defined network P4 data-plane programmable switch parallel and distributed simulation

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Generative Enhancement-based Similarity Prediction Hashing for Image Retrieval 29

Generative Enhancement-based Similarity Prediction Hashing f...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Cao, Yuan Meng, Fanlei Wu, Xiangyu Wang, Zijie Ocean University of China School of Computer Science and Technology Qingdao China

ISBN: (纸本)9798350330717

Hashing is frequently used in approximate nearest neighbor search due to its storage and search efficiency. On account of the bottleneck of traditional learning to hash methods, deep-based learning to hash has gained quite a popularity among researchers recently. While such methods show a promising performance gain by utilizing deep neural networks into its end-to-end training process to generate compact binary codes, the intrinsic connections between components make it unfeasible to optimize the architecture significantly. Subject to noise interference and absence of similarity labels of training data, normal unsupervised deep models even carry a noticeable deviation at representation learning stage. By integrating Generative Adversarial network, this paper presents a novel architecture for generating compact hash codes from an extended set derived from original images while using the benefit of similarity prediction. Extensive experiments conducted on three benchmarks, NUS-WIDE, CIFAR-10, and MS-COCO illustrate that our method outperforms state-of-the-art image retrieval models and generates high-quality binary hash codes seamlessly. © 2023 IEEE.

关键词： generative model hashing image retrieval unsupervised learning

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Game-Theoretic Analysis of (Non-)Refundable Fees in the Lightning network 29

Game-Theoretic Analysis of (Non-)Refundable Fees in the Ligh...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Kumble, Satwik Prabhu Epema, Dick Roos, Stefanie TU Delft Netherlands University of Kaiserslautern-Landau Germany

ISBN: (纸本)9798350330717

In PCNs, nodes that forward payments between a source and a receiver are paid a small fee if the payment is successful. The fee is a compensation for temporarily committing funds to the payment. However, payments may fail due to insufficient funds or attacks, often after considerable delays of up to several days, leaving a node without compensation. Furthermore, attackers can intentionally cause failed payments, e.g., to infer private information (like channel balances), without any cost in fees. In this paper, we first use extensive form games to formally characterize the conditions that lead to rational intermediaries refusing (or agreeing) to forward payments. An intermediary's decision to forward or not depends on the probability of failure, which they approximate based on past experience. We then propose and analyze an alternative fee model that allows the sender to determine and pay a fraction of the fee to intermediaries in a non-refundable manner. A rational sender chooses the fraction such that the intermediaries' utility for forwarding the payment exceeds their utility for not forwarding. Our simulation study, based on real-world Lightning snapshots, confirms that our novel mechanism can increase the probability of successful payments by 12% and decrease routing fees for senders by about 6% if all nodes behave rationally. Furthermore, previously cost-free probing attacks now require that the attacker pays 1500 satoshis for every 1 million satoshis inferred. © 2023 IEEE.

关键词： Extensive Form Games Payment Channel networks Payment Fees Probing attacks

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dBFC: Destination-based Backpressure Flow Control for Incast 29

dBFC: Destination-based Backpressure Flow Control for Incast

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Guan, Zewei Li, Wenxin He, Xin Zhang, Song Li, Keqiu Tianjin University College of Intelligence and Computing China

ISBN: (纸本)9798350330717

Incast happens in data center networks (DCNs) when many senders simultaneously send flows to one receiver. Incast occurs frequently and compromises flow performance severely. However, widely deployed end-to-end congestion control (CC) protocols are inefficient at handling incast, as they rely on delayed congestion signals and can not isolate incast flows. Recently, researchers have favored per-hop flow control protocols since they could achieve fast reaction and incast isolation. Nevertheless, these impressive advantages are impractical because of hardware resource *** this paper, we present a new per-hop flow control protocol dBFC (Destination-based Backpressure Flow Control), which achieves these advantages within limited hardware resources. Our key insight is that the number of destinations reached by queued packets in a port is minuscule. dBFC is compatible with existing CC protocols. We use large-scale NS3 simulations to evaluate dBFC. In our evaluation, compared with deployed CC protocols and the state-of-the-art per-hop flow control protocol, dBFC reduces the maximum buffer occupancy by 5 - 23× and thus provides up to 21× lower flow completion time of small flows in limited hardware resources. © 2023 IEEE.

关键词： datacenter network incast per-hop flow control

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Dual Contrastive Learning for Multi-Behavior Recommendation 29

Dual Contrastive Learning for Multi-Behavior Recommendation

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Jiahui Zhu, Yanmin Jing, Mengyuan Zang, Tianzi Chen, Qinghua Shanghai Jiao Tong University China Nanjing University of Aeronautics and Astronautics China Wenzhou Polytechnic China

ISBN: (纸本)9798350330717

Most existing collaborative filtering-based recommendation models only use a single type of interactive data. However, in real-world scenarios, there is more than one type of user interaction behavior. Although numerous recommendation models already use multi-behavior to make recommendation, there are still two limitations for them: i) the scarcity of supervised interaction data under the target behavior;ii) unable to obtain high-order collaborative relation due to over-smoothing. In order to solve the above problems, we propose a multi-behavior recommendation model, D ual C ontrastive L earning (DCL), based on contrastive learning. In DCL, a graph neural network is used to learn the local representations of users and items under multiple behaviors, while a hypergraph neural network is used to capture the global item dependency. In particular, we design two contrastive learning strategies to reinforce the quality of representations. Behavior-wise contrastive learning captures the knowledge that can be transferred between different behaviors. As well as item-wise contrastive learning captures the dependencies between item representations from different perspectives. The effectiveness of our method is verified on three real-world datasets, and the recommendation performance of DCL is superior to the various state-of-the-art recommendation models. At the same time, we also verify the robustness of our model for users with sparse interactions. © 2023 IEEE.

关键词： Collaborative Filtering Contrastive Learning Graph Neural network Multi-behavior Recommendation

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