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A distributed Cache Mechanism of HDFS to Improve Learning Performance for Deep Reinforcement Learning

A Distributed Cache Mechanism of HDFS to Improve Learning Pe...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Yongqiang Gao Shunyi Feng Zhenkun Li College of Computer Science Inner Mongolia University Hohhot China

Hadoop distributed file system (HDFS) has been widely used in deep reinforcement learning due to its high reliability and high performance. However, the dataset for training deep reinforcement learning models is usually composed of a large number of small files, which will cause the severe performance degradation of HDFS. Although many methods have been proposed to address small files problem in HDFS, none of them considers correlations between small files for training deep reinforcement learning models. To improve the learning performance for deep reinforcement learning, this paper proposes a new HDFS architecture, which uses a distributed cache mechanism to improve data access efficiency for training deep reinforcement learning models. The experimental results show that the proposed HDFS with distributed cache mechanism can achieve better learning performance compared to the traditional HDFS without cache and with centralized caching mechanism.

关键词： Deep learning Training Degradation Energy consumption File systems Clustering algorithms distributed databases

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Malicious URL Detection with distributed Representation and Deep Learning 21

Malicious URL Detection with Distributed Representation and ...

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21st international conference on New Trends in Intelligent Software Methodologies, Tools and techniques, SoMeT 2022

作者： Do, Nguyet Quang Selamat, Ali Lim, Kok Cheng Krejcar, Ondrej Universiti Teknologi Malaysia Kuala Lumpur Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Universiti Teknologi Malaysia Johor Bahru Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Kajang Malaysia

ISBN: (纸本)9781643683164

There exist numerous solutions to detect malicious URLs based on Natural Language processing and machine learning technologies. However, there is a lack of comparative analysis among approaches using distributed representation and deep learning. To solve this problem, this paper performs a comparative study on phishing URL detection based on text embedding and deep learning algorithms. Specifically, character-level and word-level embedding were combined to learn the feature representations from the webpage URLs. In addition, three deep learning models, including Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM), were constructed for effective classification of phishing websites. Several experiments were conducted and various evaluation metrics were used to assess the performance of these deep learning models. The findings obtained from the experiments indicated that the combination of the character-level and word-level embedding approach produced better results than the individual text representation methods. Also, the CNN-based model outperformed the other two deep learning algorithms in terms of both detection accuracy and execution time. © 2022 The authors and IOS Press. All rights reserved.

关键词： Websites

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Indian Sign Language Alphanumeric Hand Gestures Recognition Using Deep Learning techniques 5

Indian Sign Language Alphanumeric Hand Gestures Recognition ...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Mahendran, Shakana Mahendran, Ramya Vavuniya Campus of the University of Jaffna Department of Physical Science Fas Vavuniya Sri Lanka EUS. Information Technology Center Trincomalee Campus Trincomalee Sri Lanka

ISBN: (纸本)9798331530983

Alphanumeric sign language recognition plays a vital role in enabling effective communication between individuals who are deaf and the general population. This research focuses on classifying alphanumeric hand gestures in Indian Sign Language using real-time image processing. The study collects diverse hand shape images captured under varying backgrounds and lighting conditions, making it suitable for real-world applications. In the preprocessing stage, images are cropped and resized to a standardized 128×128 pixel format to ensure uniformity in the dataset. A two-pronged approach is adopted to classify the collected images. Sequential and parallel CNNs are employed to capture spatial and channel information effectively. Additionally, a channel attention module enhances the model's ability to identify crucial channel information. The Deep Convolutional Neural Network (DCNN) employed in this study achieved remarkable accuracy, reaching 99.976%. This outcome demonstrates the superior performance of this approach compared to other classification methods, whether utilizing real-time or non-laboratory experimental data. This research contributes to developing an efficient, accurate, and cost-effective system for classifying alphanumeric signs in Indian Sign Language. It facilitates easy and fast communication between individuals who are deaf and those who are not, promoting inclusive interactions. © 2025 IEEE.

关键词： Convolutional neural networks

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Adaptive distributed Convolutional Neural Network Inference at the Network Edge with ADCNN 20

Adaptive Distributed Convolutional Neural Network Inference ...

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49th international conference on parallel processing (ICPP)

作者： Zhang, Sai Qian Lin, Jieyu Zhang, Qi Harvard Univ Cambridge MA 02138 USA Univ Toronto Toronto ON Canada Microsoft Redmond WA USA

ISBN: (纸本)9781450388160

The emergence of the Internet of Things (IoT) has led to a remarkable increase in the volume of data generated at the network edge. In order to support real-time smart IoT applications, massive amounts of data generated from edge devices need to be processed using methods such as deep neural networks (DNNs) with low latency. To improve application performance and minimize resource cost, enterprises have begun to adopt Edge computing, a computation paradigm that advocates processing input data locally at the network edge. However, as edge nodes are often resource-constrained, running data-intensive DNN inference tasks on each individual edge node often incurs high latency, which seriously limits the practicality and effectiveness of this model. In this paper, we study the problem of distributed execution of inference tasks on edge clusters for Convolutional Neural Networks (CNNs), one of the most prominent models of DNN. Unlike previous work, we present Fully Decomposable Spatial Partition (FDSP), which naturally supports resource heterogeneity and dynamicity in edge computing environments. We then present a compression technique that further reduces network communication overhead. Our system, called ADCNN, provides up to 2.8x speed up compared to state-of-the-art approaches, while achieving a competitive inference accuracy.

关键词： Edge computing Convolutional neural networks distributed inference

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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IEEE international conference on Joint Cloud Computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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parallel Pattern Matching over Brotli Compressed Network Traffic

Parallel Pattern Matching over Brotli Compressed Network Tra...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiuwen Sun Guangzheng Zhang Di Wu Qingying Yu Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Pattern matching is a crucial technique for network traffic detection applications. As a fundamental computation model used by pattern matching, the finite state automata execute sequential matching due to the state dependence among transitions. Meanwhile, most services tend to compress their data to improve transmission or storage efficiency. The increased compressed data challenges the straightforward method of matching the whole decompressed data and incurs data dependence among the compression encodings. The related approaches either leverage techniques to break the state dependence of matching uncompressed data or accelerate matching compressed data in a single-threaded manner without considering the state and data dependence. None of them can perform parallel matching over compressed data. This paper provides PETALS, a parallel pattern matching method over Brotli compressed network traffic. PETALS partitions the original compressed traffic into fixed- length blocks for parallel matching and patches the broken compression encodings crossing blocks to break the data dependence. Then, it merges the compressed traffic matching method into path fusion, an enumerative parallelization of finite state automata, to present parallel matching over compressed traffic. Evaluation using real-world network traffic and regular expressions shows that PETALS can raise the speedup from 1.53x to 3.53x of the state-of-the-art parallelization schemes on a 56-cores machine.

关键词： Privacy Instruction sets Computational modeling Automata Telecommunication traffic Programming parallel processing

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Bandwidth-Aware Page Placement in NUMA 34

Bandwidth-Aware Page Placement in NUMA

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34th IEEE international parallel and distributed processing Symposium (IPDPS)

作者： Gureya, David Neto, Joao Karimi, Reza Barreto, Joao Bhatotia, Pramod Quema, Vivien Rodrigues, Rodrigo Romano, Paolo Vlassov, Vladimir Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal KTH Royal Inst Technol Stockholm Sweden Emory Univ Atlanta GA 30322 USA Univ Edinburgh Edinburgh Midlothian Scotland Grenoble INP ENSIMAG Grenoble France

ISBN: (纸本)9781728168760

Page placement is a critical problem for memory-intensive applications running on a shared-memory multiprocessor with a non-uniform memory access (NUMA) architecture. State-of-the-art page placement mechanisms interleave pages evenly across NUMA nodes. However, this approach fails to maximize memory throughput in modern NUMA systems, characterized by asymmetric bandwidths and latencies, and sensitive to memory contention and interconnect congestion phenomena. We propose BWAP, a novel page placement mechanism based on asymmetric weighted page interleaving. BWAP combines an analytical performance model of the target NUMA system with on-line iterative tuning of page distribution for a given memory-intensive application. Our experimental evaluation with representative memory-intensive workloads shows that BWAP performs up to 66% better than state-of-the-art techniques. These gains are particularly relevant when multiple co-located applications run in disjoint partitions of a large NUMA machine or when applications do not scale up to the total number of cores.

关键词： non-uniform memory access page placement memory-intensive applications

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Signature-Based Secure Trajectory Similarity Search 20

Signature-Based Secure Trajectory Similarity Search

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20th IEEE international conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Teng, Yiping Shi, Zhan Zhao, Fanyou Ding, Guohui Xu, Li Fan, Chunlong Shenyang Aerosp Univ Sch Comp Large Scale Distributed Syst Lab Shenyang Liaoning Peoples R China

ISBN: (纸本)9781665416580

In recent years, the computation and processing of trajectories have attracted both academic and industrial communities to study and develop techniques and applications due to the popularization of mobile devices. To offload the data management, it is motivated to outsource the trajectory data to the cloud for achieving great cost savings and flexibility. However, directly outsourcing trajectory data may arise serious privacy concerns. To address the problem, we define the problem of the secure trajectory similarity search over encrypted trajectory data and propose a secure trajectory similarity search approach based on a bi-directional similarity measurement. In this approach, we first propose a secure squared point to line-segment distance computation protocol to facilitate the precious and secure trajectory similarity computations. Furthermore, to improve the search performance, we propose a secure trajectory filtering method based on signature matching to filter out the dissimilar trajectories before the expensive computations on the ciphertext. Based on the proposed protocols and methods, we present a signature-based secure trajectory similarity search processing algorithm to retrieve the similarity results without revealing information about the trajectories. Finally, we theoretically analyze the computational complexity and security guarantees of the proposed approach and conduct extensive experiments on both real and synthetic datasets to test the search performance under various experimental settings.

关键词： Trajectories

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TEEp: Supporting Secure parallel processing in ARM TrustZone 26

TEEp: Supporting Secure Parallel Processing in ARM TrustZone

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Li, Zinan Li, Wenhao Xia, Yubin Zang, Binyu Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China

ISBN: (纸本)9781728190747

Machine learning applications are getting prevelent on various computing platforms, including cloud servers, smart phones, IoT devices, etc. For these applications, security is one of the most emergent requirements. While trusted execution environment (TEE) like ARM TrustZone has been widely used to protect critical prodecures including fingerprint authentication and mobile payment, state-of-the-art implementations of TEE OS lack the support for multi-threading and are not suitable for computing-intensive workloads. This is because current TEE OSes are usually designed for hosting security critical tasks, which are typically small and non computing-intensive. Thus, most of TEE OSes do not support multi-threading in order to minimize the size of the trusted computing base (TCB). In this paper, we propose TEEp, a system that enables multi-threading in TEE without weakening security, and supports existing multi-threaded applications to run directly in TEE. Our design includes a novel multi-threading mechanism based on the cooperation between the TEE OS and the host OS, without trusting the host OS. We implement our system based on OP-TEE and port it to two platforms: a HiKey 970 development board as mobile platform, and a Huawei Hi1610 ARM server as server platform. We run TensorFlow Lite on the development board and TensorFlow on the server for performance evaluation in TEE. The result shows that our system can improve the throughput of TensorFlow Lite on 5 models to 3.2x when 4 cores are available, with 13.5% overhead compared with Linux on average.

关键词： Multi-threading parallel processing TEE Trust-Zone

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Artificial Intelligence in Smart Mobile Communication Systems: A Review

Artificial Intelligence in Smart Mobile Communication System...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Adebajo-Akinpelu Victor O Moses Oluwafemi Onibonoje Ayodeji Olalekan Salau Department of Electrical/Electronics and Computer Engineering Afe Babalola Univerisity Ado-Ekiti Ado-Ekiti Nigeria Dept of Electrical/Electronics & Computer Engineering Afe Babalola University Ado Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Communication and information service systems are undergoing rapid advancements driven by the evolution of telecommunications and computer technology. The convergence of these fields has led to significant growth in digital networks, which has spurred extensive research and exploration. However, optimizing the use of these networks with minimal additional costs—while integrating features such as video conferencing, email services, and remote database access— remains a substantial technological challenge. This study addresses the limitations of current mobile communication networks by exploring potential enhancements through the application of smart computing solutions, particularly artificial intelligence (AI). The research methodology involves a comprehensive review of existing mobile communication technologies and their integration with AI. This includes examining advancements in AI and machine learning (ML) as applied to mobile networks, with a focus on improving performance, flexibility, and automation. The results of the review indicate that AI and ML have significant potential to enhance network operations. By leveraging vast data generated within 5G systems, these technologies can optimize resource utilization, improve service quality, and support diverse emerging applications. Despite these advancements, challenges such as reliability, speed trade-offs, complexity, privacy, and security persist. The findings highlight the need for further research in AI, including deep neural network acceleration, parallel computing, cloud computing, and distributed deep learning systems. Such research is crucial for enabling mobile communication networks to achieve higher throughput and ultra-low latency. Conclusively, while AI-enabled communication systems offer promising solutions to many challenges, simplifying AI implementation is essential for practical deployment. This will ensure that future communication networks can effectively meet the growing demands and evolving

关键词： Deep learning Privacy Reviews 5G mobile communication parallel processing Throughput Complexity theory Security Resource management Artificial intelligence

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