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A Generic Efficient Scientific Workflow Engine for the Optimizations of Run-time Execution

A Generic Efficient Scientific Workflow Engine for the Optim...

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1st IEEE international Conference on software Services engineering (IEEE SSE)

作者： Bai, Changxin Liu, Junwen Tahabilder, Anik Imran, M. M. Lu, Shiyong Che, Dunren Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Southern Illinois Univ Dept Comp Sci Carbondale IL USA

ISBN: (纸本)9798350340754

Workflow has proven to be a highly effective computing model for a variety of scientific applications, offering flexible data types and unstructured parallelism that surpasses simple parallel execution models such as MapReduce. However, current workflow management systems in cloud computing environments experience unnecessary delays in task execution due to the separation of task execution and data transfer processes, which causes a child task to wait until all its predecessor tasks complete, rather than waiting only for necessary input data becoming ready. The goal of this paper is to eliminate the unnecessary delay of child tasks in a workflow, which is achieved through a new workflow engine architecture that separates workflow planner from workflow executor in the general framework of the DATAVIEW scientific workflow management system. This new engine architecture can be generalized and applied to other workflow systems. Our design integrates a new task release mechanism based on a data dependency model with the workflow executor of DATAVIEW. This approach enables prompt task launching once input data becomes available, instead of waiting for all predecessor tasks to finish. The architecture employs distributed algorithms for implementing the workflow executor and the task executors, performing various optimization on data movement, task movement, and communication among different subsystems. The experiments show that our new architecture based on the new task release model can significantly reduce overall execution time of a workflow in DATAVIEW.

关键词： Workflow Engine Workflow Execution parallel Processing Task Releasing Optimization

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FedEntropy: Efficient Federated Learning for Non-IID Scenarios Using Maximum Entropy Judgment-based Client Selection 21

FedEntropy: Efficient Federated Learning for Non-IID Scenari...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud Computing, 16th IEEE international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ling, Zhiwei Yue, Zhihao Xia, Jun Wang, Ting Chen, Mingsong Lian, Xiang East China Normal University MoE Engineering Research Center of SW/HW Co-design Tech. and App. Shanghai China Kent State University Department of Computer Science Kent United States

ISBN: (纸本)9798350329223

Although various techniques have been proposed for Federated Learning (FL) to address its problem of low classification accuracy in non-IID scenarios, most of them neglect both i) distinct data distribution characteristics of heterogeneous devices (i.e., clients), and ii) benefits and hazards of local models for global model aggregation. In this paper, we present FedEntropy, an efficient FL approach with a novel two-stage dynamic client selection scheme that fully takes the above two factors into account. Unlike existing FL methods, FedEntropy firstly selects clients with high potential for benefiting global model aggregation in a coarse manner, and then further filters out inferior clients from such selected clients by using our proposed maximum entropy judgment method. Based on the pre-collected soft labels of the selected clients, FedEntropy only aggregates those local models that can maximize the overall entropy of their soft labels, thus effectively improving global model accuracy while reducing the overall communication overhead. Comprehensive experimental results on well-known benchmarks demonstrate both the superiority of FedEntropy and its compatibility with state-of-the-art FL methods. © 2023 IEEE.

关键词： client selection Federated Learning maximum entropy judgment non-IID data distribution

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Optimizing MEC System Performance Through M/M/m Queueing Model Analysis

Optimizing MEC System Performance Through M/M/m Queueing Mod...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jiale Liang Xiaowei Ma Houjian Ding Zhenli He School of Software Yunnan University Kunming China Hongta Tobacco (Group) Co. Ltd China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper investigates the performance of Mobile Edge Computing (MEC) systems using the M/M/m queuing model, focusing on how server configurations and queuing rules affect task response times on edge servers. The study considers two types of tasks with different processing requirements: special tasks and general tasks. The study analyzes the response time under two queuing rules: first-come-first-served (FCFS) and head-of-line (HOL) priority. By evaluating the impact of server execution speed and the number of cores, this work provides insights for optimizing task scheduling and resource management in MEC environments, which ultimately improves system performance and reduces latency.

关键词： Analytical models distributed processing Multi-access edge computing System performance Computational modeling Focusing Servers Time factors Resource management Queueing analysis

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BZSim: Fast, Large-Scale Microarchitectural Simulation with Detailed Interconnect Modeling

BZSim: Fast, Large-Scale Microarchitectural Simulation with ...

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2024 IEEE international symposium on Performance Analysis of systems and software, ISPASS 2024

作者： Strikos, Panagiotis Ejaz, Ahsen Sourdis, Ioannis Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798350376388

Modeling of on-chip interconnects in microarchitectural simulations is becoming more important. Chips continue to increase their number of cores, i.e., via 3D stacking and multi-chiplet integration, and their performance is gradually more affected by their network. However, existing simulation alternatives are either too slow for large systems or lack support for modeling interconnects, which is on its own a computationally intensive task. This work offers an open-source solution that integrates an accurate, widely used network simulator into an existing fast, parallel microarchitectural simulator enhanced with a new mechanism to make network modeling lightweight. It is based on the observation that a significant fraction of the network traffic has low contention and on the conjecture that the latency of such traffic can be calculated analytically with good accuracy. The proposed approach offers a mechanism to detect low contention traffic and analytically calculate its latency reducing the overheads of network simulation. In essence, it offers a knob for trading simulation accuracy for speed. This tradeoff is explored by demonstrating 2-4× faster simulations within 3-5% error in normalized IPC and 10-20% in average packet latency. Our simulation setup is an order of magnitude faster than an optimistic gem5 setup with point-to-point interconnects and 3× slower than a ZSim setup without network modeling. Our approach is further used to assess the impact of Networkon-Chip designs on system performance and shows that a 32-core system is on average 1.2× faster when using the current state of the art NoC instead of a baseline NoC. © 2024 IEEE.

关键词： Network-on-chip

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs 21

Efficient Large Scale Reverse-time Migration Imaging Computa...

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作者： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 IEEE.

关键词： Graphics processing unit

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks 33

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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33rd Euromicro international Conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Rendel, Jonathan Balte, William Kurunathan, Harrison Ali, Hazem Ismail Roque, Alexandre Dos Santos Morais, Wagner Ourique De Fazeli, Mahdi Halmstad Sweden Polytechnic Institute of Porto CISTER/ISEP Portugal Federal University of Rio Grande do Sul - UFRGS Graduate Program of Electrical Engineering Porto Alegre Brazil

ISBN: (纸本)9798331524937

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called "ZeroCAN"which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over 99% and a false positive rate below 0.01% during normal operation in most cases. © 2025 IEEE.

关键词： Anomaly detection CAN bus IDS Feature engineering Zero-day

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Money Laundering Detection on Ethereum: Applying Traditional Approaches to New Scene 29

Money Laundering Detection on Ethereum: Applying Traditional...

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

作者： Yu, Yunmei Wu, Jiajing Lin, Dan Fu, Qishuang Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Sun Yat-Sen University School of Software Engineering Guangzhou China

ISBN: (纸本)9798350330717

With the continuous evolution of blockchain technology, cryptocurrency platforms such as Ethereum have emerged as centers for digital asset transactions and smart contracts deployment. However, this nascent financial ecosystem also introduces potential money laundering risks. The traditional financial industry has accumulated significant antimoney laundering (AML) experience and technical means for monitoring and detecting money laundering activities. Yet, the adaptability of these established AML algorithms in the context of blockchain remains unclear. This paper aims to investigate the practical adaptability of traditional AML algorithms on Ethereum data through empirical experiments. We gather eight real-world money laundering case datasets collected from Ethereum and conduct experiments using three traditional AML algorithms on these datasets. We evaluate the performance of these algorithms from various angles, including precision, recall, and the distribution of detected accounts' labels in comparison to the original datasets. It turns out algorithms demonstrate distinct performance in diverse money laundering cases, indicating that the adaptability of traditional AML algorithms on Ethereum data presents certain adaptability and limitations. Holoscope's accuracy demonstrates the value of dense subgraph properties in Ethereum money laundering detection, and further research can be conducted based on this model framework combined with the money laundering characteristics of Ethereum. Our study provides valuable insights for strengthening AML mechanisms on blockchain platforms and offers guidance for further research on detecting money laundering accounts in blockchain environments. © 2023 IEEE.

关键词： Anti-money laundering Blockchain Security Ethereum Money Laundering Detection

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A Study on the Performance of WebAssembly Versus JavaScript in an SVG Editor Environment

A Study on the Performance of WebAssembly Versus JavaScript ...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Jason Jeremy Wijadi Bryn Ghiffar Ida Bagus Kerthyayana Manuaba Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Scalable Vector Graphics (SVG) is an XML-based image format for two-dimensional diagrams. Made up of vectors, SVG enable image and behavior manipulation through a versatile XML-based format that can be created and edited with any text editor and many graphical editing software. With the advent of the web, SVG editing applications have become increasingly available to the public. However, in order to be able to serve users the ability to edit vector graphics in real-time, the performance of the editor plays a significant role in ensuring a smooth user experience. The goal of this study is in comparing the performance of WebAssembly and JavaScript for an SVG editor environment. Our results show that the WebAssembly implementation outperforms JavaScript in terms of execution time and memory usage.

关键词： Graphics parallel processing Vectors User experience software Real-time systems

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A Cooperative Multi-AUV Mobile Scheme with Optical Communication via DDPG Approach 29

A Cooperative Multi-AUV Mobile Scheme with Optical Communica...

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

作者： Li, Mengzhen Luo, Hanjiang Li, Xiang Ruby, Rukhsana Tao, Hang Wu, Kaishun Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Hong Kong

ISBN: (纸本)9798350330717

To implement high-speed wireless communication from the deep ocean to the sea surface leveraging multiple autonomous underwater vehicles (AUVs) with underwater wireless optical communication (UWOC) technique is an emerging and promising technology that enables real-time data collection for accurate underwater exploration and monitoring, e.g., coordinated moving targets tracking. However, multi-hop UWOC is more susceptible to beam misalignment and positional uncertainty caused by external interference in the harsh marine environments. To address these challenges, we design a cooperative movement scheme for multiple AUVs based on deep reinforcement learning (DRL) approach to realize robust and reliable optical communication under mobile target tracking scenarios. In this scheme, we first model the optical channel with optical noise and then analyze the link performance of multiple AUVs to meet the bit error rate (BER) requirements. Afterwards, we map the cooperative optical communication problem to a Markov decision process (MDP) by incorporating the extended Kalman filter (EKF) technique to enhance effective communication. Finally, we propose a cooperative control strategy for multiple mobile AUVs based on deep deterministic policy gradient (DDPG). Through extensive simulations, it is demonstrated that the proposed algorithm is effective in achieving reliable underwater optical communication under mobile scenarios. © 2023 IEEE.

关键词： Autonomous Underwater Vehicles Deep Deterministic Policy Gradient Extended Kalman Filter Underwater Wireless Optical Communication

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CDCP: A Framework for Accelerating Dynamic Channel Pruning Methods 29

CDCP: A Framework for Accelerating Dynamic Channel Pruning M...

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

作者： Xiang, Zhaohong Luo, Yigui Xie, Yin She, Haihong Liang, Weiming Zhao, Laigang Tongji University School of Software Engineering Shanghai China Company Limited China

ISBN: (纸本)9798350330717

Dynamic channel pruning is a technique aimed at reducing the theoretical computational complexity and inference latency of convolutional neural networks. Dynamic channel pruning methods introduce complex additional modules for dynamically selecting channels for images. Due to the additional modules, dynamic channel pruning methods never achieve optimal acceleration effect in real world. To address this problem, we propose Consecutive Dynamic Channel Pruning (CDCP), a novel dynamic channel pruning framework unified for almost all dynamic pruning methods designed for continuous image processing. The core idea of CDCP stems from our observation that adjusting the network for all frames in semantically continuous scenes is unnecessary since adjacent frames often share similar network structures in dynamic channel pruning. CDCP introduces a simple binary classifier to determine whether the network structure needs to be adjusted for a new frame. Our method can also be used for semantically non-continuous image processing tasks with a slightly lower probability of model reuse. We validate the effectiveness of CDCP on three dynamic channel pruning methods and better acceleration effects are achieved when applied them with CDCP to the semantically continuous Waymo dataset, the nuScenes dataset, and the semantically discontinuous COCO dataset. © 2023 IEEE.

关键词： dynamic channel pruning model acceleration network structure

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