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distributed Runtime Verification under Partial Synchrony 24

Distributed Runtime Verification under Partial Synchrony

引用

24th international conference on Principles of distributed Systems, OPODIS 2020

作者： Ganguly, Ritam Momtaz, Anik Bonakdarpour, Borzoo Michigan State University East LansingMI United States

ISBN: (纸本)9783959771764

In this paper, we study the problem of runtime verification of distributed applications that do not share a global clock with respect to specifications in the linear temporal logics (LTL). Our proposed method distinguishes from the existing work in three novel ways. First, we make a practical assumption that the distributed system under scrutiny is augmented with a clock synchronization algorithm that guarantees bounded clock skew among all processes. Second, we do not make any assumption about the structure of predicates that form LTL formulas. This relaxation allows us to monitor a wide range of applications that was not possible before. Subsequently, we propose a distributed monitoring algorithm by employing SMT solving techniques. Third, given the fact that distributed applications nowadays run on massive cloud services, we extend our solution to a parallel monitoring algorithm to utilize the available computing infrastructure. We report on rigorous synthetic as well as real-world case studies and demonstrate that scalable online monitoring of distributed applications is within our reach. © Ritam Ganguly, Anik Momtaz, and Borzoo Bonakdarpour;licensed under Creative Commons License CC-BY 24th international conference on Principles of distributed Systems (OPODIS 2020).

关键词： Formal verification

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distributed Deep Learning for Medical Image processing in Cloud Environments

Distributed Deep Learning for Medical Image Processing in Cl...

引用

Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), international conference on

作者： Neeraj Varshney Parul Madan Anurag Shrivastava C Praveen Kumar A Kakoli Rao Amit Srivastava Department of Computer Engineering and Applications GLA University Mathura Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana Lloyd Institute of Engineering and Technology Greater Noida Lloyd Law College Greater Noida

The incorporation of distributed deep learning for medical image processing in cloud settings is the subject of this study. The findings demonstrate the high viability and significant performance advantages realized by cloud-based distributed systems, notably significant processing time savings, outstanding diagnostic accuracy, as well as improved scalability. The consequences for security and privacy have been discussed, with a focus on effective safeguards for private medical information. There is a void in the literature about resource and cost-effectiveness optimization tactics used in cloud-based systems. Future research must concentrate on resource optimization tactics for economic sustainability, study developing security risks and privacy techniques, and incorporate real-world implementations in order to improve this topic. This study informs the use of distributed deep learning in cloud-based medical image processing as well as adds to the body of knowledge in healthcare technology.

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Optimizing Secure Software Development Lifecycle with parallel Temporal Convolutional Network for Vulnerability Detection

Optimizing Secure Software Development Lifecycle with Parall...

引用

distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： S. Padmakala Mohammad Naseera Sudhakar K Hassan Mohamed Ali Banoth Ramesh Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of CSE Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Smart contracts are one of the software application which is developed based on blockchain, are extensively used in Internet of Things, finance management, and other applications. However, development of smart contracts increases, meanwhile vulnerability attacks elated to the software also increases which occur often. To overcome this limitation, parallel Temporal Convolutional Network (PTCN) is proposed for the vulnerability detection in secure software development. By using parallel temporal convolutions, the detection model process multiple time steps at the same time, that helps to identify patterns effectively. Firstly, Smartbugs datasets are used for acquiring data regarding the vulnerable behaviors in a software development. After that these data are pre-processed by regularization method and fed to word embedding for converting text data to vector data. Finally, from these vector data, the proposed PTCN model detects the vulnerability efficiently. The experimental results of the proposed PTCN method attained accuracy of 93.26% which is higher than the existing detection methods such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN).

关键词： Support vector machines Convolutional codes Accuracy Smart contracts Software algorithms Vectors Software Data models Convolutional neural networks Software development management

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A Fast Image Matching Approach in Fatigue Testing of Wall Plate Structures

A Fast Image Matching Approach in Fatigue Testing of Wall Pl...

引用

IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Zhang Wendong Shi Baoquan Chen Xianmin Lv Yuanbo National Key Laboratory of Strength and Structural Integrity AVIC Aircraft Strength Research Institute Xi'an China School of Mechano-Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

To address the challenges in motion vector estimation due to local non-uniform deformation of specimens during fatigue tests, this paper proposes an image matching method that combines template sub-image techniques with a Kalman filter. This method employs a mesh motion estimation model, initially using the Kalman filter to predict the matching point positions of mesh nodes. Subsequently, template matching is utilized to achieve precise matching of these nodes. The algorithm is accelerated through GPU-CPU heterogeneous parallel computing technology. Test results indicate that the GPU-accelerated image matching method attains the accuracy of the CPU-based algorithm while significantly reducing processing time, thereby fully meeting the real-time requirements of online image feature matching applications.

关键词： Accuracy Deformation Image matching Signal processing algorithms Fatigue Prediction algorithms Vectors Real-time systems Kalman filters Testing

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A Study of Clustering Algorithms on Big Data using Spark

A Study of Clustering Algorithms on Big Data using Spark

引用

Data Engineering and Communication Systems (ICDECS), international conference on

作者： A Vijay S Shilpa Department of Computer Applications BMS College of Engineering Bangalore India

Big data clustering on Spark is a practical method that makes use of Apache Spark’s distributed computing capabilities to handle clustering tasks on massive datasets such as big data sets. Using the unsupervised learning technique of clustering, related data points are grouped together to find underlying structures and patterns. Spark’s strong platform overcomes the performance and scalability restrictions of conventional clustering techniques, allowing effective and scalable clustering operations. The performance and dependability of big data clustering on Spark are further improved by fault tolerance and in-memory processing. By employing popular clustering algorithms like k-means, hierarchical, bisecting, GMM, and BIRCH, researchers and practitioners can extract valuable insights from extensive datasets, making informed decisions and unlocking meaningful patterns on a large scale.

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Research on the Design of Digital Signal processing Algorithm Based on Spark parallel Calculation

Research on the Design of Digital Signal Processing Algorith...

引用

international conference on Electronics, Electrical and Information Engineering 2021, ICEEIE 2021

作者： Ming, Yifan Li, Rui School of Physics and Electronics Central South University Hunan China School of Mathematics and Computational Science Hunan University of Science and Technology Hunan China

Digital signal processing as a key and difficult point of network technology research and development, currently commonly used content such as LabVIEW. But from a practical point of view, while these techniques can be used to process real-time signals, they can't handle historical offline data. The Spark parallel computing studied in this paper can be used to process offline signals. Therefore, on the basis of understanding the development trend of Spark parallel computing framework, the distributed Mallat algorithm is analyzed based on Spark parallel computing engine, and the application performance of the corresponding algorithm is verified. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Digital signal processing

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Fpga Implementation of Leading Zero Counter for Floating Point Operations

Fpga Implementation of Leading Zero Counter for Floating Poi...

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Artificial Intelligence and Signal processing (AISP), INDIA, international conference on

作者： Lakshmi Madhumati. G Bhargavi Velivela Hussain Sharif Shaik Baladitya Pasam Dept. Electronics and Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Vijayawada India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

This paper highlights the creation of a leading zero counter designed for floating-point calculations using field programmable gate arrays (FPGAs). A key component of data sketching and floating-point arithmetic is the leading zero counter (LZC). Though more and more of these applications are being implemented on FPGAs, current LZC architectures are designed to target application-specific integrated circuits, with certain LZC implementations specifically made for FPGA structures. By effectively recognizing leading zero bits in the exponent portion of floating-point numbers, the design seeks to improve the speed and efficiency of floating-point computations. Better resource usage is achieved by the FPGA-based version by utilizing hardware acceleration techniques and parallel processing, which is essential for high-performance and real-time computing *** designs for various bit widths are given and discussed in further detail. The proposed new LZC is hybrid LZC and the findings indicate that notable decreases in the FPGA resources needed are obtained that reach 11.53 % LUTs saving for 32-bit vectors and 16.66 % LUTs saving for 64-bit vectors and also implements additional 128-bit length with 101 LUTs which reduces 15.833% LUTs.

关键词： Computer architecture Signal processing parallel processing Logic gates Vectors Real-time systems Table lookup Field programmable gate arrays Hardware acceleration Floating-point arithmetic

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***: Integrated parallel Density-Based Clustering Through Locality-Sensitive Hashing 28th

***: Integrated Parallel Density-Based Clustering Through Lo...

引用

28th international conference on parallel and distributed Computing (Euro-Par)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9783031125973;9783031125966

Locality-sensitive hashing (LSH) is an established method for fast data indexing and approximate similarity search, with useful parallelism properties. Although indexes and similarity measures are key for data clustering, little has been investigated on the benefits of LSH in the problem. Our proposition is that LSH can be extremely beneficial for parallelizing high-dimensional density-based clustering e.g., DBSCAN, a versatile method able to detect clusters of different shapes and sizes. We contribute to fill the gap between the advancements in LSH and density-based clustering. We show how approximate DBSCAN clustering can be fused into the process of creating an LSH index, and, through parallelization and fine-grained synchronization, also utilize efficiently available computing capacity. The resulting method, ***, can support a wide range of applications with diverse distance functions, as well as data distributions and dimensionality. We analyse its properties and evaluate our prototype implementation on a 36-core machine with 2-way hyper threading on massive data-sets with various numbers of dimensions. Our results show that *** effectively complements established state-of-the-art methods by up to several orders of magnitude of speed-up on higher dimensional datasets, with tunable high clustering accuracy through LSH parameters.

关键词： Clustering algorithms

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

引用

16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, Thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： distributed cloud

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Accelerating DBSCAN Algorithm with AI Chips for Large Datasets 21

Accelerating DBSCAN Algorithm with AI Chips for Large Datase...

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50th international conference on parallel processing (ICPP)

作者： Ji, Zhuoran Wang, Cho-Li Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781450390682

DBSCAN is a popular clustering algorithm, which shows great success in many real-world applications. Its advantages come at the expense of massive computation, especially for computing the distance matrix. Driven by deep learning, many Artificial Intelligence (AI) chips have been developed. With efficient matrix multiplication units, AI chips can significantly accelerate the distance calculation. However, DBSCAN also needs to identify and count the neighbors for each point. It is challenging for most AI chips due to overspecialization. Moreover, the increasing data size and the limited device memory capacity force DBSCAN to follow a mini-batch manner. It results in a high data transfer overhead, which further hinders the performance of DBSCAN on AI chips. In this paper, we propose two novel techniques to address the challenges of accelerating the DBSCAN algorithm with AI chips: (1) new neighbor identification algorithms using bitwise operations only, while traditional solutions require the compare-and-select operations that are weakly supported in AI chips;and (2) two speculative execution strategies to reduce the data transfer overhead induced by mini-batches. Evaluations show that deploying distance matrix calculation to tensor cores achieves 2.61x speedup on Nvidia RTX 3090. On Huawei Ascend 310, our neighbor identification algorithms achieve 17.88x throughout of using CPUs for neighbor identification. The speculative execution strategies further reduce the execution time by 15.1% on average for normal datasets and up to 99.0% for sparse datasets.

关键词： Tensor core AI chip parallel computing DBSCAN clustering

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