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Reliability Optimization Scheduling and Energy Balancing for Real-Time Application in Fog computing Environment 15th

Reliability Optimization Scheduling and Energy Balancing for...

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15th International symposium on Advanced Parallel Processing Technologies, APPT 2023

作者： Liu, Ruihua Huang, Huijuan He, Yulei Guo, Xiaochuan Yan, Can Dai, Junhao Wu, Wufei School of Mathematics and Computer Science Nanchang University Jiangxi Nanchang China School of Information Engineering Nanchang University Jiangxi Nanchang China

ISBN: (纸本)9789819978717

Fog computing has the characteristics of stronger localized computing power and less data transmission load, thus better meeting the high energy efficiency, reliability, and real-time response requirements required by intelligent connected vehicle technology applications. Currently, research on fog computing task scheduling has become a hot topic, with existing research mainly focusing on low energy consumption or high real-time parallel task scheduling, which cannot meet the high reliability requirements in intelligent connected vehicle scenarios. therefore, this paper establishes a fog computing task model based on Directed acyclic graph (DAG) to achieve accurate definition of energy, time and reliability. To achieve quantitative optimization of time and reliability indicators under energy constraints, a fog computing task scheduling algorithm was proposed and compared with existing scheduling algorithms. then, the proposed algorithm is used to solve the DAG task list optimization problem based on fast Fourier transform (FFT) and Gaussian elimination (GE) structure. the experimental results show that compared with the existing ECLL method, ECLLRS has a more significant effect in satisfying the real-time and reliability of the system under the premise of limited energy budget. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reliability

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Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets 15

Application of IMVR Convolutional Neural Networks to Classif...

引用

15th International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Shuai, Yuanzhen Xin, Ning Hasan, Md Maruf Hu, Bintao Dai, Tianhong Liu, Hengyan Xi'an Jiaotong-Liverpool University School of Ai and Advanced Computing Suzhou China Xi'an Jiaotong-Liverpool University School of Internet of Things Suzhou China The University of Aberdeen School of Natural and Computing Sciences Aberdeen United Kingdom

ISBN: (纸本)9798350308693

Despite extensive research, remote sensing image classification remains a challenging issue within the field of remote sensing image analysis. Achieving a balance between classification accuracy and computational efficiency remains challenging, as traditional methods often face difficulties in attaining both high speed and precision simultaneously. To tackle this dilemma, we propose a method named IMVR which significantly reduces the computational burden while maintaining validity. this method enhances the richness and accuracy of high-dimensional feature representations through its output. Extensive experiments are conducted on the UC Merced Land-Use Dataset to demonstrate that our method can substantially improve classification performance and efficiency in comparison to traditional methods. © 2023 IEEE.

关键词： Land use

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SQLStateGuard: Statement-Level SQL Injection Defense Based on Learning-Driven Middleware 15

SQLStateGuard: Statement-Level SQL Injection Defense Based o...

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15th Annual ACM symposium on Cloud computing, SoCC 2024

作者： Liu, Xin Huang, Yuanyuan Wang, Tianyi Li, Song Niu, Weina Shen, Jun Zhou, Qingguo Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Gansu Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Computing and Information Technology University of Wollongong WollongongNSW Australia Kansai University Osaka Suita Japan

ISBN: (纸本)9798400712869

SQL injection is a significant and persistent threat to web services. Most existing protections against SQL injections rely on traffic-level anomaly detection, which often results in high false-positive rates and can be easily bypassed by attackers. this paper introduces SQLStateGuard, the world's first middleware-driven statement-level SQL injection defense approach, to address these issues. the SQLStateGuard uses a custom SQL middleware based on the idea of Runtime Application Self-Protection to capture raw SQL statements. these statements are then analyzed by SQLSG-Net, a database-oriented detection network based on gated linear units. If SQLSG-Net detects malicious SQL statements, the SQL middleware will block them. Experiments show that the detection accuracy of SQLStateGuard exceeds 99%, outperforming existing approaches, and it can identify the type of a specific SQL injection. Additionally, SQLStateGuard has no fingerprint and does not respond to SQL syntax errors, making it more challenging for attackers to gather information. this paper also presents a novel dataset generation process for SQLStateGuard and shares two statement-level SQL injection datasets with the research community, including over 145,000 malicious SQL statements categorized by the type of SQL injection. © 2024 ACM.

关键词： Middleware

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A Comparative Analysis of YOLOv8 and YOLO-NAS for Identifying Fetal Intracranial Structures in Pregnancy 15th

A Comparative Analysis of YOLOv8 and YOLO-NAS for Identif...

引用

15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Singh Gill, Navtegh Wadhwa, Stuti Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab147004 India

ISBN: (纸本)9783031810794

Standard/non-standard (S-NS) plane classification is a key step for Nuchal Translucency measurement and early Down Syndrome assessment in fetus during pregnancy weeks 11–14. To classify S-NS planes, fetal intracranial structures have to be identified with high precision and low latency. this paper presents a comparative study between YOLOv8 and YOLO-NAS for the identification of fetal intracranial structures during pregnancy weeks 11–14. Using a diverse dataset comprising 1528 2D sagittal-view ultrasound images from 1519 females, we evaluate the performance of these state-of-the-art object detection models. the dataset includes meticulously labelled instances of crucial fetal intracranial structures such as thalami, nasal bone, palate, 4th ventricle, cisterna magna, NT (nuchal translucency), nasal tip, nasal skin and midbrain. through comprehensive experiments and analysis, this research sheds light on the suitability of these YOLO variants for accurate and efficient detection of these structures in early-stage pregnancies, providing valuable insights for improving prenatal healthcare and diagnosis. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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LLM-Driven Cross-Platform Code Generation for Polyhedral Optimized NPDP Codes 15th

LLM-Driven Cross-Platform Code Generation for Polyhedral Op...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Palkowski, Marek Faculty of Computer Science and Information Systems West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71210 Poland

ISBN: (纸本)9783031856990

this paper explores using large language models (LLM) for automatic and source-to-source programming across various cross-platform languages and libraries, including generating CUDA code for high performance computing (HPC). Our proposed solution aims to demonstrate the capabilities of ChatGPT in generating valid CUDA code based on existing OpenMP code obtained from a polyhedral compilers Traco and Dapt. Specifically, we intend to showcase the model’s proficiency in producing equivalent and valid GPU code. Furthermore, we extend the application of our solution by prompting the AI model to generate similar code for another input benchmark, emphasizing its adaptability and versatility across different scenarios. this approach seeks to highlight the model’s capacity to provide automated and efficient solutions for diverse programming challenges. We chose non-trivial kernels from the non-serial polyadic dynamic programming (NPDP) benchmark with non-uniform loops. the focus is on ensuring code validity and understanding limitations in obtaining valid CUDA code. Additionally, we assess the efficiency and scalability of NVIDIA A100 GPU codes generated by artificial intelligence (AI) models, comparing them with optimized Intel Xeon Gold CPU codes from polyhedral optimizers. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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COMPASS: SRAM-Based computing-in-Memory SNN Accelerator with Adaptive Spike Speculation 57

COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with...

引用

57th Annual IEEE/ACM International symposium on Microarchitecture, MICRO 2024

作者： Wang, Zongwu Liu, Fangxin Yang, Ning Huang, Shiyuan Li, Haomin Jiang, Li Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Qi Zhi Institute China AI Institute Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence China

ISBN: (纸本)9798350350579

Brain-inspired spiking neural networks (SNNs) are considered energy-efficient alternatives to conventional deep neural networks (DNNs). By adopting event-driven information processing, SNNs can significantly reduce the computational demands associated with DNNs, while still achieving comparable performance. However, current SNNs primarily prioritize high accuracy by constructing complex neuron models that generate sparse spikes. Unfortunately, this approach results in low energy efficiency and high latency, posing a significant challenge for deploying SNNs at the edge. Furthermore, the dominant computation in SNNs, which involves spike-wise Accumulate-Compare operations, is well-suited for computing-in-Memory (CIM) architectures. However, exploiting high parallel processing and spike sparsity in CIM-based SNN accelerators is challenging due to the irregularity and time dependency of spikes. To address these limitations, the paper proposes COMPASS, a SRAM-based CIM architecture for efficient SNNs. We first introduce an efficient method to exploit irregular sparsity for both input spikes (explicit) and output spikes (implicit). this is achieved through a speculation mechanism that exploit dynamic spike patterns, enabling lean hardware for sparsity utilization. Additionally, the CIM architecture is carefully modified to facilitate dynamic spike pattern generation and exploitation with minimal overhead. Moreover, we design an adaptive dataflow with temporal spike representation tailored for input/output spikes, reducing memory footprint and enabling parallel execution. Comprehensive evaluation results demonstrate that COMPASS can achieve 26.7x end-to-end speedup over recent SNN accelerators hardware implementation with up to 386.7x less energy per inference. © 2024 IEEE.

关键词： Compasses (magnetic)

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Chasing Carbon: the Elusive Environmental Footprint of computing 27

Chasing Carbon: The Elusive Environmental Footprint of Compu...

引用

27th IEEE International symposium on high-performance computer architecture (HPCA)

作者： Gupta, Udit Kim, Young Geun Lee, Sylvia Tse, Jordan Lee, Hsien-Hsin S. Wei, Gu-Yeon Brooks, David Wu, Carole-Jean Harvard Univ Cambridge MA 02138 USA Facebook Inc Menlo Pk CA 94025 USA Arizona State Univ Tempe AZ USA

ISBN: (纸本)9781665422352

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. this paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. this work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems.

关键词： Data center mobile energy carbon footprint

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HEAPS: A Novel Energy-Based Configurable HPC Scheduler 15th

HEAPS: A Novel Energy-Based Configurable HPC Scheduler

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Stafford, Esteban Cruz, Luis Bosque, Jose Luis Department of Computer Engineering and Electronics Universidad de Cantabria Santander Spain

ISBN: (纸本)9783031856990

high performance computing (HPC) is conducted in large computer clusters to support complex scientific applications and simulations. these infrastructures are notoriously power-hungry, creating a pressing need to optimise their usage. Additionally, modern clusters are often considered heterogeneous as they consist of groups of nodes with varying characteristics. this article presents a novel scheduling algorithm, the Heterogeneous Energy-Aware Pairing Scheduler (HEAPS), which aims to reduce energy consumption by performing an a priori estimation of the energy consumption of jobs across available nodes to achieve optimal job-node pairings. the evaluation compares its behaviour to that of different schedulers for homogeneous and heterogeneous clusters, demonstrating that certain configurations of HEAPS can reduce energy consumption in various cluster types and sizes, and in several cases, improve makespan and energy efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy consumption Energy efficiency Heterogeneous clusters Workload manager

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Short-Time Traffic Flow Prediction Based on K-means++ and LSTM 20

Short-Time Traffic Flow Prediction Based on K-means++ and LS...

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20th IEEE International symposium on Parallel and Distributed Processing with Applications, 12th IEEE International Conference on Big Data and Cloud computing, 12th IEEE International Conference on Sustainable computing and Communications and 15th IEEE International Conference on Social computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2022

作者： Li, Jia Xu, Gang Cheng, Yuan Huang, Baoqi Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing Hohhot010021 China College of Computer Science Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9781665464970

With the gradual increase in the number of vehicles owned by people, the problem of urban traffic congestion is becoming more and more serious. Timely and accurate traffic flow prediction helps travel vehicles to plan their routes reasonably, avoid congested roads and reduce environmental pollution. In order to take full advantage of the daily periodicity of the traffic flow of a road section, K-means++ with improved initial clustering centroids is applied to analyze the similarity of the daily traffic flow patterns and classify the traffic flow patterns with day as the time interval. One can separate the traffic patterns affected by different factors, and the other can exclude individual days containing abnormal data trends formed by unexpected road conditions, which can save the deep learning model from the interference of other irrelevant data features. Finally, a Long Short-Term Memory (LSTM) model is combined to predict the traffic flow value at the next time point based on the data of the previous hour. In the experiment, the K-means-LSTM model can significantly reduce the prediction error of short-time traffic flow and has better prediction performance by comparing with the prediction results of the support vector machine (SVR) model optimized by genetic algorithm (GA) and the LSTM model. © 2022 IEEE.

关键词： Long short-term memory

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PMBS 2024: 15th IEEE International Workshop on performance Modeling, Benchmarking, and Simulation of high performance computer Systems

Proceedings of SC 2024-W: Workshops of the International Con...

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Proceedings of SC 2024-W: Workshops of the International Conference for high performance computing, Networking, Storage and Analysis 2024年 1358-1361页

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