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parallel and distributed Machine Learning Techniques for Anomaly Detection systems 10

Parallel and Distributed Machine Learning Techniques for Ano...

引用

10th international Scientific conference "Information Technology and Implementation", IT and I 2023

作者： Khlevna, Iulia Koval, Bohdan Taras Shevchenko National University of Kyiv Volodymyrska str. 60 Kyiv01033 Ukraine

Efficient processing of extensive datasets is crucial in data-driven applications, particularly for anomaly detection. This article explores the application of parallel and distributed machine learning techniques to enhance anomaly detection systems. parallel training, including model and data parallelism, significantly reduces processing times in data preprocessing, feature engineering, and model training. Experiments demonstrate notable efficiency improvements, especially for extensive datasets. distributed training is crucial in scenarios with time-intensive training, storage constraints, data localization, or RAM limitations. The approach to parallelize learning process has been demonstrated, which allowed to speed up the processing time by 2x via allocating 4x resources. Also, it depicts how the process can be spread across multiple machines using distributed techniques. In conclusion, this article emphasizes the pivotal role of parallel and distributed machine learning techniques in accelerating the development of anomaly detection systems. These techniques empower organizations to address challenges posed by extensive datasets and real-time data streams effectively. As data-driven applications evolve, these advanced computing methods promise more effective and timely responses to anomalies, solidifying their significance in the era of big data. © 2023 Copyright for this paper by its authors.

关键词： Machine learning

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Efficiency Improvement of parallel Source Inverters in Islanded AC Microgrid 10

Efficiency Improvement of Parallel Source Inverters in Islan...

引用

10th IEEE international conference on Power and Energy, PECon 2024

作者： Hasan, Md Akib Hossain, Md Showkot Roslan, Mohd Azrik Azmi, Azralmukmin Leong, Jenn Hwai Nazib, Ahmad Afif Faculty of Electrical Engineering and Technology Universiti Malaysia Perlis Perlis Malaysia Faculty of Electrical Technology and Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9798350361971

The limited capacity of a single inverter has led to the implementation of parallel inverters for interconnecting renewable energy generation systems. Droop control is commonly used for operating parallel inverters;however, conventional droop control has a low conversion efficiency under heavy loads. Therefore, this paper designed an efficient control strategy to enhance the conversion efficiency of parallel inverters. The employed method incorporates an internal current and voltage control loop based on a synchronous reference frame, along with conventional PI controllers. Additionally, Particle Swarm Optimization (PSO) is utilized to determine the optimal conversion efficiency of the parallel inverters. In this approach, the distributed generation (DG) unit adjusts its control mode to modify system voltage and frequency in response to dynamic load conditions. Efficiency improvements were observed in the range of 0.19% to 0.41% across various initial power distribution ratios, highlighting the efficacy of optimized droop parameters in maintaining robust and efficient power systems. Simulation results using MATLAB/SIMULINK demonstrate that the proposed control system improves system efficiency ©2024 IEEE.

关键词： Microgrids

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Speedcode: Software Performance Engineering Education via the Coding of Didactic Exercises

Speedcode: Software Performance Engineering Education via th...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Kaler, Tim Chen, Xuhao Wheatman, Brian Curtis, Dorothy Hoppe, Bruce Schardl, Tao B. Leiserson, Charles E. MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA Johns Hopkins Univ Baltimore MD USA Connect Associates Boston MA USA

ISBN: (纸本)9798350364613;9798350364606

This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain hands-on experience in software performance engineering and parallel programming by completing short programming exercises. Speedcode challenges users to develop fast multicore solutions for short programming problems and evaluates their code's performance and scalability in a quiesced cloud environment. Speedcode supports parallel programming using OpenCilk, task-parallel computing platform that is open-source and easy to program, teach and use for research. Speedcode aims to reduce barriers to learning and teaching software performance engineering. It allows users to run and evaluate their code on modern multicore machines from their own computer without installing any software. This provides users an easy introduction to the topic, and enables teachers to more easily incorporate lessons on software performance engineering into their courses without incurring the onerous overhead of needing to setup computing environments for their students.

关键词： education online learning platforms parallel programming software performance engineering

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New Algorithms for distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

引用

23rd international conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Machine learning

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CAPTURE: Memory-Centric Partitioning for distributed DNN Training with Hybrid parallelism 30

CAPTURE: Memory-Centric Partitioning for Distributed DNN Tra...

引用

30th international conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Dreuning, Henk Verstoep, Kees Bal, Henri E. van Nieuwpoort, Rob V. Univ Amsterdam Amsterdam Netherlands Vrije Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands

ISBN: (纸本)9798350383225

Deep Learning (DL) model sizes are increasing at a rapid pace, as larger models typically offer better statistical performance. Modern Large Language Models (LLMs) and image processing models contain billions of trainable parameters. Training such massive neural networks incurs significant memory requirements and financial cost. Hybrid-parallel training approaches have emerged that combine pipelining with data and tensor parallelism to facilitate the training of large DL models on distributed hardware setups. However, existing approaches to design a hybrid-parallel partitioning and parallelization plan for DL models focus on achieving high throughput and not on minimizing memory usage and financial cost. We introduce CAPTURE, a partitioning and parallelization approach for hybrid parallelism that minimizes peak memory usage. CAPTURE combines a profiling-based approach with statistical modeling to recommend a partitioning and parallelization plan that minimizes the peak memory usage across all the Graphics Processing Units (GPUs) in the hardware setup. Our results show a reduction in memory usage of up to 43.9% compared to partitioners in state-of-the-art hybrid-parallel training systems. The reduced memory footprint enables the training of larger DL models on the same hardware resources and training with larger batch sizes. CAPTURE can also train a given model on a smaller hardware setup than other approaches, reducing the financial cost of training massive DL models.

关键词： HPC Deep Learning Hybrid parallelism GPU Memory

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A parallel and distributed Quantum SAT Solver Based on Entanglement and Teleportation 30th

A Parallel and Distributed Quantum SAT Solver Based on Entan...

引用

30th international conference on Tools and Algorithms for the Construction and Analysis of systems (TACAS) Held as Part of the 27th European Joint conferences on Theory and Practice of Software (ETAPS)

作者： Lin, Shang-Wei Wang, Tzu-Fan Chen, Yean-Ru Hou, Zhe Sanan, David Teo, Yon Shin Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia Singapore Inst Technol InfoComm Technol Cluster Singapore Singapore Continental Automot Singapore Singapore

ISBN: (纸本)9783031572487;9783031572494

Boolean satisfiability (SAT) solving is a fundamental problem in computer science. Finding efficient algorithms for SAT solving has broad implications in many areas of computer science and beyond. Quantum SAT solvers have been proposed in the literature based on Grover's algorithm. Although existing quantum SAT solvers can consider all possible inputs at once, they evaluate each clause in the formula one by one sequentially, making the time complexity O(m), linear to the number of clauses m, per Grover iteration. In this work, we develop a parallel quantum SAT solver, which reduces the time complexity in each iteration to constant time O(1) by utilising extra entangled qubits. To further improve the scalability of our solution in case of extremely large problems, we develop a distributed version of the proposed parallel SAT solver based on quantum teleportation such that the total qubits required are shared and distributed among a set of quantum computers (nodes), and the quantum SAT solving is accomplished collaboratively by all the nodes. We prove the correctness of our approaches and evaluate them in simulations and real quantum computers.

关键词： Iterative methods

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Robust optimization for minimizing total tardiness on unrelated parallel machine scheduling 29

Robust optimization for minimizing total tardiness on unrela...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Han, Yibo Qi, Qi Li, Ni Li, Jinqing Hainan University School of Computer Science and Technology Haikou570228 China Hainan Normal University School of Mathematics and Statistics Haikou571158 China Hainan Normal University Key Laboratory of Data Science and Smart Education of Ministry of Education Haikou571158 China

ISBN: (纸本)9798350330717

This paper investigates a scheduling problem with due times for unrelated parallel machines, incorporating uncertain setup times and processing times. The tardiness of jobs can be easily overlooked in production scheduling problems when dealing with uncertainty, potentially leading to disruptions in the production plan and longer production cycles. To address this issue, we propose a robust model to minimize the total tardiness while considering the uncertainty of setup times and processing times, as well as incorporating the constraint of due times. We then convert the model into a linear robust formulation and solve it using Gurobi. Through experimental comparisons between the deterministic model and the proposed robust model under uncertain conditions, the results demonstrate that the robust model exhibits superior performance in handling uncertain situations. © 2023 IEEE.

关键词： Deterministic model Due time constraints Robust optimization Total tardiness Uncertain processing and setup times Unrelated parallel machines

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A Comparative Evaluation of Zero Knowledge Proof Techniques 10

A Comparative Evaluation of Zero Knowledge Proof Techniques

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10th international conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Rostamkolaei Motlagh, Seyed Mohsen Pahl, Claus Barzegar, Hamid R. El Ioini, Nabil Free University of Bozen-Bolzano Bolzano39100 Italy University of Nottingham Semenyih43500 Malaysia

ISBN: (纸本)9789897587504

Common to many distributed systems such as the Internet-of-Things (IoT) is decentralisation, often with a growing number of devices with diverse computational capabilities and security requirements. If integrated into critical applications, ensuring secure communication and data integrity is critical. In particular, privacy and security concerns are growing with the rise of these architectures. Zero-Knowledge Proof (ZKP) techniques are solutions to improve security without compromising on privacy. While the advantages of ZKP techniques are well-documented, it is important to consider the inherent limitations of these distributed environments, such as restricted processing power, memory capacity, and energy constraints, when aiming to solve security concerns. Here, we select two prominent ZKP techniques, Bulletproof and zk-STARK, and experimentally compare a number of their variants for a set of architecture-specific assessment criteria. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： parallel processing systems

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autotarget*: A distributed Robot Operating System Framework for Autonomous Aerial Swarms 21

autotarget*: A Distributed Robot Operating System Framework ...

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21st international conference on Ubiquitous Robots (UR)

作者： Adoni, Wilfried Yves Hamilton Fareedh, Junaidh Shaik Lorenz, Sandra Gloaguen, Richard Kuehne, Thomas D. Ctr Adv Syst Understanding eV CASUS HZDR Gorlitz Germany Helmholtz Inst Freiberg Resource Technol eV HIF H Freiberg Germany

ISBN: (纸本)9798350361087;9798350361070

Robot Operating System (ROS) has proven itself as a viable framework for developing robot-related applications. It offers features such as hardware abstraction, low-level device support, inter-process communication, and useful libraries for autonomous robot systems. Concerning aerial robots, commonly called unmanned aerial vehicles (UAV) or drones, ROS provides unfortunately very basic functions. Moreover, it does not guarantee real-time operation, as it runs under Linux. Consequently, it is difficult to implement advanced ROS applications that involve a swarm of drones that need to communicate with each other to carry out a common mission. This paper proposes an extended version of the ROS framework called autotarget*, which provides a set of efficient functions designed for distributed operation on multiple UAVs flying at the same time. autotarget* relies on a multi-tier architecture with a decentralized communication layer, enabling intra-UAV messaging as well as the scalability of swarm UAVs. It has a set of daemons whose feature is to regulate the swarm's consensus control and failover policy to ensure convergence towards a common goal. Experiments with real-world swarms revealed that autotarget* is portable and satisfies the performance requirements for collaborative mission applications. We further conducted a coverage planning mission using the parallel back-and-forth algorithm, which demonstrated the efficiency of the framework in terms of time and energy. Our work should pave the way for an open-source environment dedicated to simplifying collaborative ROS application development, particularly for multi-UAV systems.

关键词： Robot Operating System

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

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

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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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