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Enhanced Slicing based Deep Learning architecture for 5G networks in distributed Learning

Enhanced Slicing based Deep Learning architecture for 5G net...

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international conference on computing and networking Technology (ICCNT)

作者： Atul Dadhich Amar Shankar Aarsi Kumari Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Food Technology School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper gives a distributed deep getting-to-know (DDL) architecture for 5 G community cutting. DDL is an allotted method which has been a success in schooling massive-scale deep mastering fashions. It applies the ideas of disbursed computing to reduce the dimensions of the statistics being trained at the same time as education samples in parallel on different computing assets. The proposed solution in this paper exploits the improvements in 5G networks’ virtualization skills, which include community cutting, to provide a green architecture which allows DDL fashions to be taught with dispensed resources to limit the training time. The proposed structure consists of three predominant modules: a useful resource-cutting manager, a dispensed training cluster, and a dispensed inference cluster. The resource-reducing manager allows dynamic aid cutting throughout numerous assets (e.g. GPU, CPU). The distributed schooling cluster includes workers distributing the training duties across the sources. The allotted inference cluster is used to perform inference on the trained fashions throughout more than one allotted computing source. The advantages of the proposed structure are that it allows green disbursed schooling, green disbursed inference, and value-green education of complex deep gaining knowledge of models. As such, it may be used to educate huge and complex models with decreased prices and also boost the schooling throughput. Additionally, 5G community cutting presents flexibility in useful resource provision.

关键词： Training 5G mobile communication Computational modeling Scalability Layout Graphics processing units Computer architecture Throughput Dynamic scheduling Virtualization

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A Social Network Analysis of User-Organised Community on Digital Music Platform

A Social Network Analysis of User-Organised Community on Dig...

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Manlin Wang Ting Jiang School of Computer and Cyber Sciences School of Music and Recording Arts Communication University of China Beijing China Electronic Information Engineering Beihang University Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

In the mobile Internet era, users can achieve social interaction and spontaneously form music communities through the music content, digital services and interactive functions provided on digital music platforms. Previous research on music communities has mainly focused on economic, cultural and social fields. However, music communities are complex and multidimensional, and qualitative analyses make it difficult to demonstrate the network structure and attributes of the communities comprehensively. Therefore, this paper uses social network analysis to study the “family” in the Chinese online music platform WeSing as the research object. The social network constructed by users sending flowers, gifts, and comments to each other within the community is studied through data statistics and analysis. The study found that the music communities organised and participated by users on digital music platforms are relatively loose social networks. However, within the community, driven by common music interests and recognition of the value of the community, users continue to gather and deepen their participation in the community around music events. Several different sub-groups have gradually formed within the community. This study also shows that digital music platforms are important in generating music communities and disseminating music culture.

关键词： Economics Social networking (online) Network analyzers Flowering plants Internet Resource management Global communication

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Systematically Inferring I/O Performance Variability by Examining Repetitive Job Behavior 21

Systematically Inferring I/O Performance Variability by Exam...

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international conference for High Performance computing, networking, Storage and Analysis (SC21)

作者： Costa, Emily Patel, Tirthak Schwaller, Benjamin Brandt, Jim M. Tiwari, Devesh Northeastern Univ Boston MA 02115 USA Sandia Natl Labs Albuquerque NM USA

ISBN: (纸本)9781450384421

Monitoring and analyzing I/O behaviors is critical to the efficient utilization of parallel storage systems. Unfortunately, with increasing I/O requirements and resource contention, I/O performance variability is becoming a significant concern. This paper investigates I/O behavior and performance variability on a large-scale high-performance computing (I IPC) system using a novel methodology that identifies similarity across jobs from the same application leveraging an I/O characterization tool and then, detects potential I/O performance variability across jobs of the same application. We demonstrate and discuss how our unique methodology can be used to perform temporal and feature analyses to detect interesting I/O performance variability patterns in production UPC systems, and their implications for operating/managing large-scale systems.

关键词： Storage systems HPC HPC I/O performance variability machine learning distributed computing systems

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Identification of distributed Denial of Service Attacks Employing L-BFGS Tuning

Identification of Distributed Denial of Service Attacks Empl...

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international conference on computing and networking Technology (ICCNT)

作者： Vikas Singh Vasudev Dehalwar Jyoti Bharti Department of CSE MANIT Bhopal Bhopal India

Internet speed and capacity are crucial components of any communication technology in the modern era. Customers should expect excellent speed and a lot of bandwidth. A DDoS (distributed denial of service) assault, which is a well-known attack, is the biggest threat in communication. DDoS attacks limit the user’s speed and bandwidth. In order to produce this work, we used the updated dataset, CICDDoS2019, and undertook tests using a variety of machine learning methods. We investigated which features had the strongest relationships with the analysis’s projected classes. This research proposal offers a machine learning-based L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm-based DDoS assault detection method. An HPC Python platform was used to carry out the simulation and experiment for the suggested research project. The accuracy, true and false negatives, along with true and false positives, confusion matrix structures, and other outcome measures are all evaluated in this study. The proposed method shows better results as compared to other previous methods. Index Terms—DDoS attack, MLP Classifier, LBFGS, Machine Learning.

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Securing Smart Home IoT devices using Hyperledger Fabric

Securing Smart Home IoT devices using Hyperledger Fabric

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international conference on computing and networking Technology (ICCNT)

作者： R Pranesh B Lalith Guptha K Aswinkumar B Pramoth K Abirami Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The rapid growth of Smart Home applications has broadened the scope for a wide range of security concerns. Blockchain technology, specifically Hyperledger Fabric, offers a promising solution. The proposed model explores its application, leveraging the Fablo toolkit to enhance the security of Smart Home IoT devices. The decentralized nature of Hyperledger Fabric, with features such as its consensus mechanism and chaincode, helps mitigate common security issues like unauthorized access and device tampering. The resultant tamperproof network, coupled with favorable metrics like latency and transaction throughput, underscores the robustness of the proposed model. Our model promises low latency and high transaction throughput. The metrics assist the model in securely and efficiently handling a large number of transactions without compromising on speed and security.

关键词： Technological innovation distributed ledger Smart cities Smart homes Computer architecture Throughput Fabrics Consensus protocol Security Internet of Things

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Financial Fraud Detection for Credit Card Transactions Using Apache Spark

Financial Fraud Detection for Credit Card Transactions Using...

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Sustainable Communication Networks and Application (ICSCNA), international conference on

作者： Pratyush Jha Srijan Srivastava Tanishq Gandhi Gouthaman. P Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The financial sector's rapid adoption of digital transformation has made identifying fraud a progressively complex challenge, since modern fraudulent activities present significant risks to institutions globally. This paper presents a novel methodology for detecting financial fraud that integrates big data analytics and machine learning techniques within a distributed environment. With an extensive number of actual financial transactions, the system uses Apache Spark for real-time data processing and machine learning algorithms to accurately classify fraudulent transactions. This method effectively addresses two major issues: efficiently processing large data sets and managing the severe class imbalance between fraudulent and non-fraudulent transactions. Collectively, these solutions provide a scalable and resilient framework for improving fraud detection in the digital era.

关键词： Support vector machines Machine learning algorithms Accuracy Finance Cluster computing Big Data Credit cards Real-time systems Fraud Stakeholders

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Proceedings of 2020 IEEE/ACM Workshop on Machine Learning in High Performance computing Environments, MLHPC 2020 and Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, AI4S 2020 - Held in conjunction with SC 2020: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of 2020 IEEE/ACM Workshop on Machine Learning in...

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6th IEEE/ACM Workshop on Machine Learning in High Performance computing Environments, MLHPC 2020 and 1st Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, AI4S 2020

ISBN: (纸本)9780738110783

The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machine learning in python using MPI library: a case study with MVAPICH2-GDR;deep learning-based low-dose tomography reconstruction with hybrid-dose measurements;EventGraD: event-triggered communication in parallel stochastic gradient descent;a benders decomposition approach to correlation clustering;high-bypass learning: automated detection of tumor cells that significantly impact drug response;deep generative models that solve PDEs: distributed computing for training large data-free models;automatic particle trajectory classification in plasma simulations;reinforcement learning-based solution to power grid planning and operation under uncertainties;and predictions of steady and unsteady flows using machine-learned surrogate models.

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Combating the distributed Network Attacks using A Proposed Progressive Analyzer based on an Ensemble Learning Framework 13

Combating the Distributed Network Attacks using A Proposed P...

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13th international conference on computing Communication and networking Technologies, ICCCNT 2022

作者： Thomas, Merly Meshram, B.B. Veermata Jijabai Technological Institute Dept. of Computer Engineering Mumbai India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Internet and other networks provide a consistent stage for individuals and items to interface and communicate with each other. This continuous interaction results in automatic cooperation of millions of various appliances in a global network resulting in undeniable benefits by making our lives simpler. Since there is no way to be completely sure that a system is impenetrable by cybersecurity threats millions of people has been affected by various malicious activities. The current network forensics procedures can't distinguish and trace the steps and techniques of attacks since they erase the entire history of what they did after completing their task. Hence, the work proposed in this paper mainly focuses on designing an optimal analyzer, which can constantly combat network attacks. Initially, a sublunary-dimensional-based Clustering approach is proposed, which deals with the clusters having different types of attributes of network traffic to avoid missing any important information from unlabeled packet samples. After that, the clustered data is fed to the Validator to check for the Botnet characteristics in the clustered information. Moreover, arbitrary learning based on the ensemble approach has been proposed in this work, which determines several important Botnet measures: Botnet Size, Bandwidth Consumption, Network Diameter, Robustness, etc., to improve the precision of predictive analytics. © 2022 IEEE.

关键词： Network security

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On Scaling Neuronal Network Simulations Using distributed computing

On Scaling Neuronal Network Simulations Using Distributed Co...

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High Performance computing, networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Vladimir Omelyusik Khawar Shehzad Tyler Banks Praveen Rao Satish S. Nair University of Missouri

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

We investigated the computational capabilities of FABRIC, a nationwide research infrastructure with nearly 40 sites, for scaling neuroscience simulations. From the hardware standpoint, single-site characterization showed that FABRIC is a promising alternative to conventional neuroscience setups, particularly due to the availability of powerful graphics processing units (GPUs). While multi-site simulations are affected by network latency, it becomes less critical for larger networks. From the software perspective, we found that in the popular CoreNEURON library, cell distribution strategy (for parallel execution) does not affect the simulation time for biologically realistic networks, while other cases can be addressed with a minimum k-cut graph partitioning algorithm. Overall, scalability experiments revealed that FABRIC can be used to simulate networks of up to twenty-five thousand cells, with the limiting issue being GPU memory.

关键词： Neuroscience Distribution strategy Computational modeling Biological system modeling Software algorithms Graphics processing units Fabrics Hardware Software Partitioning algorithms

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SoCFlow: Efficient and Scalable DNN Training on SoC-Clustered Edge Servers 24

SoCFlow: Efficient and Scalable DNN Training on SoC-Clustere...

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29th ACM international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Xu, Daliang Xu, Mengwei Lou, Chiheng Zhang, Li Huang, Gang Jin, Xin Liu, Xuanzhe Peking Univ Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China State Key Lab Networking & Switching Technol Beijing Peoples R China Natl Key Lab Data Space Technol & Syst Beijing Peoples R China

ISBN: (纸本)9798400703720

SoC-Cluster, a novel server architecture composed of massive mobile system-on-chips (SoCs), is gaining popularity in industrial edge computing due to its energy efficiency and compatibility with existing mobile applications. However, we observe that the deployed SoC-Cluster servers are not fully utilized, because the hosted workloads are mostly usertriggered and have significant tidal phenomena. To harvest the free cycles, we propose to co-locate deep learning tasks on them. We present SoCFlow, the first framework that can efficiently train deep learning models on SoC-Cluster. To deal with the intrinsic inadequacy of commercial SoC-Cluster servers, SoCFlow incorporates two novel techniques: (1) the group-wise parallelism with delayed aggregation that can train deep learning models fast and scalably without being influenced by the network bottleneck;(2) the data-parallel mixed-precision training algorithm that can fully unleash the heterogeneous processors' capability of mobile SoCs. We have fully implemented SoCFlow and demonstrated its effectiveness through extensive experiments. The experiments show that SoCFlow significantly and consistently outperforms all baselines regarding the training speed while preserving the convergence accuracy, e.g., 1.6x-740x convergence speedup with 32 SoCs. Compared to commodity GPU (NVIDIA V100) under the same power budget, SoCFlow achieves comparable training speed but reduces energy consumption by 2.31x-10.23x with the same convergence accuracy.

关键词： SoC-Cluster distributed machine learning mixed precision training

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