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Research on Scheduling Algorithms for AI Data-Intensive Tasks in Edge Heterogeneous Environments

Research on Scheduling Algorithms for AI Data-Intensive Task...

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2023 IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2023

作者： Liu, Luanqi Xu, Zixian Wang, Yingxun Wang, Xiang School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350309614

Edge computing has become a new computing service model by virtue of its low latency and high performance characteristics. It deploys cloud server resources at the edge of the network to help mobile devices to complete data-intensive, large-scale and complex computing tasks. The heterogeneity of resources and the mobility of devices in the edge environment make the edge heterogeneous environment often suffer from unbalanced distribution of resources and loads, which leads to the reduction of system computing performance and resource utilisation. The CPU-GPU heterogeneous computing system in the edge environment has higher performance than the isomorphic system, and the existing task scheduling strategy still limits the performance of the heterogeneous system. Aiming at the above problems, a task scheduling model for CPU-GPU heterogeneous systems is designed, and a genetic algorithm based on reinforcement learning is proposed to achieve efficient allocation of AI data-intensive tasks. The Q-learning algorithm is used to achieve adaptive adjustment of parameters to solve the complex and high-dimensional task scheduling problem. Simulation experiments are carried out by Edge-cloudsim platform, compared with other methods, the results prove that the QL-GA task scheduling method proposed in this paper can provide an efficient solution to the task scheduling problem in edge heterogeneous computing systems. © 2023 IEEE.

关键词： edge computing heterogeneous computing systems Reinforcement learning task scheduling

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An Active Security Defense Strategy for Microservices based on Deep Reinforcement learning 21

An Active Security Defense Strategy for Microservices based ...

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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： Li, Yuanbo Hu, Hongchao Zhang, Shuai Cheng, Guozhen Liu, Wenyan Pla Strategic Support Force Information Engineering University China Luoyang Institute of Science and Technology China Purple Mountain Laboratories China

ISBN: (纸本)9798350329223

For container cloud and microservice architectures, the attack process is more complex and diverse. How to improve the defense effectiveness of the system while ensuring the quality of service is a huge challenge. To solve this problem, this paper proposes an active security defense strategy for container cloud and microservices based on deep reinforcement learning. This method considers both security and quality of service. We model the problem from the dynamic cleaning cycle and the number of microservices replicas, and designed a deep reinforcement learning method to solve it. The results show that the proposed method has better defense effectiveness than the DESOM and SmartSCR defense strategy. The defense efficiency is improved by 34.4% and 12.3% respectively, which verifies the feasibility and effectiveness of the proposed strategy. © 2023 IEEE.

关键词： Quality of service

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Exploring the Effectiveness of Deep learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Machine learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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An Efficient Technique for Feature Selection in learning to Rank Using NSGA-II

An Efficient Technique for Feature Selection in Learning to ...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Amala, K.J. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Feature Selection (FS) is essential for optimizing learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framework of learning to rank, specifically emphasizing a multi-objective evolutionary algorithm utilizing the listwise approach. Conventional ranking algorithms frequently experience issues with superfluous or unnecessary features, undermining their efficacy. The suggested method employs a combination of instance selection, evolutionary optimization, and ensemble techniques to choose feature subsets. The methodology seeks to reduce the feature count while enhancing ranking precision, assessed via the Normalized Discounted Cumulative Gain (NDCG) metric. Experiments were performed using LETOR 4.0 dataset (MQ2007-list and MQ2008-list), evaluating proposed method against established listwise ranking algorithms like ListMLE, ListNet, RankCosine and p-ListMLE. The findings indicate that, although there are performance discrepancies relative to current algorithms, the suggested method attains substantial feature reduction up to 60 percent while maintaining competitive ranking accuracy. © 2024 IEEE.

关键词： Optimization algorithms

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Transformer Network-based Image Segmentation using Hybrid Flower Pollination Optimization 18

Transformer Network-based Image Segmentation using Hybrid Fl...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Sivajyothi, Modugula Madhavi, K. Reddy Vishnu Vandana Devi, V. Basi Reddy, A. Tangudu, Naresh Avanija, J. Cmr Technical Campus Department of It Telangana India Mohan Babu University School of Computing Tirupati India Vr Siddhartha Engineering College Vijayawada India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Effective image segmentation is important in various fields, from medical imaging to autonomous vehicles. This paper presents an integration of Hybrid Flower Pollination Optimization (HFPO) with Transformer networks, considering strengths of global optimization and deep learning. HFPO, inspired by natural pollination processes, excels in exploring vast solution spaces, while Transformers, renowned in deep learning, capture complex patterns. This fusion enhances segmentation accuracy and computational efficiency. Balancing sophistication and computational feasibility, this approach marks a significant advancement in image analysis, promising transformative applications across industries. The experimentation and evaluation, reveal that the proposed HFPO-Transformer based model for image segmentation is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Computer Vision Deep learning Hybrid Flower Pollination Optimization Image Segmentation Transformer Networks

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From Cloud Down to Things: An Overview of Machine learning in Internet of Things 4

From Cloud Down to Things: An Overview of Machine Learning i...

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4th international conference on computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Humayoun, Muneeba Sharif, Hanan Rehman, Faisal Shaukat, Shahbaz Ullah, Muhbat Maqsood, Hadia Ali, Chaudhry Nouman Iftikhar, Razia Chandio, Adil Hussain Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan Mehran University of Engineering and Technology Szab Department of Computer Science Khairpur Mir's Pakistan Quaid e Awam University of Engineering Science and Technology Department of Electronic Engineering Larkano Pakistan

ISBN: (纸本)9798350335316

Due to the large number of things and information devices, not all IoT applications can be satisfied by processing data in the cloud. Due to the cloud's constrained ability to process and share data, edge computing, or the act of initiating IoT edge data processing and connected devices' transformation from intelligent devices to gadgets, was developed. Machine learning is the key instrument. It is important to include information inference as a continuum in the cloud-to-things approach. Reviewing machine functions that are connected to the Internet, from the cloud all the way down to embedded devices. Many uses for machines learning to handle application data management and processing responsibilities are examined. The most current machine learning apps for IoT are gathered, and they all agree on their feedback and application space. The type of data, the machine learning methods used, and the locations belong to the continuum from clouds to objects. The issues and future directions of IoT machine learning research are spoken about. Additionally, employing methods for categorization using machine learning, papers on 'machine' learning in IoT are meticulously retrieved and reviewed. Next, with the expansion of recognized subjects and application domains, difficulties and search are moving in the direction of effective machine learning for the IoT. In addition, articles on the IoT's 'machine' learning are painstakingly retrieved, then classified using machine learning methods. © 2023 IEEE.

关键词： Metadata

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Crowd Counting: An Overview

Crowd Counting: An Overview

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2024 international conference on Cloud computing and Communication engineering, CCCE 2024

作者： Jiao, Yingjie Zhang, Zhaoguo Lu, Yi Song, Wei Qin, Xingguo Ning, Yi Chen, Jinlong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Guangxi Tourism Technology Corporation Limited Guilin China Guilin University of Electronic Technology School of Continuing Education Guilin China

ISBN: (数字)9781510686717

ISBN: (纸本)9781510686700

Crowd counting is a computer vision task that involves using computer algorithms to analyze the number and distribution of crowds. With the world's population increasing, stampedes caused by too many people continue to occur frequently, and the number of researchers paying attention to the task of crowd counting has been growing *** article first introduces about the traditional crowd counting algorithm and the deep learning-driven crowd counting algorithm. It introduces two performance indicators used to assess the crowd counting algorithm, the mean absolute error (MAE, which can be represented by EMAE )and the root mean square error (RMSE, which can be represented by ERMSE ).Finally, discussed some public datasets about crowd counting work .These datasets make much contributions to the development of algorithms, and comparing the experimental results of different algorithms can help improve crowd counting algorithms. © 2024 SPIE.

关键词： Adversarial machine learning

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Helmet Guard: Engine Management and Accident Severity Assessment for Motorcycles 15

Helmet Guard: Engine Management and Accident Severity Assess...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vanga, Siva Reddy Angadala, Niharika Alajangi, Purna Gowtham Velagapudi Ramakrishna Siddhartha Engineering College Electronics and Communication Engineering Vijayawada India

ISBN: (纸本)9798350370249

Motorcycle safety systems traditionally face limitations in engine control, accident assessment, and rider protection. This paper introduces a solution that enhances engine management employs machine learning for accurate accident severity analysis, and incorporates alcohol detection with accelerometer-based tilt assessment designed for motorcycles. This project mandates helmet usage before the engine starts, ensuring the rider's safety. The system concurrently monitors critical factors such as alcohol concentration and tilt angles and deactivates the engine upon detecting irregularities. This innovative approach helps make riding motorcycles safer and smarter by overcoming previous problems. It represents a significant advancement, promising enhanced security and intelligence on the road. © 2024 IEEE.

关键词： Motorcycles

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Enhancing Credit Card Fraud Detection with Deep learning and Graph Neural Networks 15

Enhancing Credit Card Fraud Detection with Deep Learning and...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sherwin Akshay, J.G. Vinusha, T. Sharon Bianca, R. Sarath Krishna, C.K. Radhika, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Financial fraud poses a major threat to financial service institutions and clients, facilitating anomaly detection capabilities. This paper dives into deep learning models which can be implemented to detect and distinguish fraudulent transactions from normal transactions and flag anomalies. Further, conversion of tabular data to graphs have been implemented for keen understanding and improved pattern analysis of fraudulent transactions, challenges include evolving attack strategies, class imbalance and model interpretability. The proposed methods utilize a synergistic combination of ensemble learning techniques and unsupervised deep neural networks, like Auto-Encoders and Recurrent Neural Networks. In the graphical representation, this study focuses on implementing Louvain Modularity, Girvan-Newman and Label Propagation Algorithm for effectiveness in community detection. The outcomes exhibit consistently reliable results on credit card transaction datasets. A comprehensive comparative analysis follows to compare the proposed approach to traditional fraud detection methods, demonstrating its superiority in accurately identifying fraudulent transactions, adapting to emerging threats, handling high-dimensional data, and mitigating class imbalances with respect to data privacy and security. © 2024 IEEE.

关键词： Auto-Encoders CatBoost GAN GNN GRU RNN Unsupervised learning XG-Boost

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