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A Cooperative Caching Scheme in Fog/Sensor Nodes for CCN 18

A Cooperative Caching Scheme in Fog/Sensor Nodes for CCN

引用

18th IEEE international Wireless Communications and Mobile computing conference (IWCMC)

作者： Sellami, Youssef Jaber, Ghada Lounis, Ahmed Lakhlef, Hicham Bouabdallah, Abdelmadjid Sorbonne Univ Univ Technol Compiegne CNRS UMR 7253HEUDIASYC CS 60319 F-60203 Compiegne France

ISBN: (纸本)9781665467490

Current Internet of Things (IoT) and Wireless Sensor Networks (WSNs) designs struggle to meet the excessive demand for content by an increasing number of smart objects, in terms of bandwidth and energy consumption. At the same time, the presence of devices with relatively high resources in the vicinity of the IoT devices, such as fog computing, may provide a balance between the cloud and end devices. In this paper, we propose to design and implement a distributed fog caching architecture for Content-Centric networking (CCN). Such an architecture consists of sensor sub-networks connected to one or more fog nodes that manage the internal caching policy and the interactions with fog nodes for efficient content caching and retrieval. Through extensive simulations, we show that our strategy achieves better results compared to existing strategies in terms of energy consumption, cache hit ratio, network congestion, and user satisfaction rate.

关键词： Fog computing Wireless sensor networks (WSN) Content-Centric networking (CCN) Caching

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Distance Vector and Prominent Reliable Path Selection based Stochastic Routing in distributed Internet of Things 19

Distance Vector and Prominent Reliable Path Selection based ...

引用

19th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC)

作者： Abid, Quswar Abbas, Ghulam Ali, Zaiwar Abbas, Ziaul Haq Tu, Shanshan Harrath, Youssef Waqas, Muhammad GIK Inst Engn Sci & Technol Telecommun & Networking Res Ctr Topi Pakistan Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Univ Bahrain Dept Comp Engn Coll Informat Technol Zallaq Bahrain Edith Cowan Univ Sch Engn Joondalup WA Australia

ISBN: (纸本)9798350333398

Delayed delivery of packets hinders the performance of time-sensitive Internet of Things (IoT) applications and incurs increased power consumption. Stochastic routing schemes solve the problem of saving all participating nodes from getting their power drained out quickly. However, stochastic routing incurs the problem of delivery delays and reliable end-to-end delivery. This paper proposes a novel routing scheme, called Qij routing, to solve these problems. The proposed Qij routing scheme is a combination of a classic routing scheme, called Distance Vector Algorithm, with a novel re-definition of the cost of a link to find the best path from source to destination. Qij takes into account the wireless link reliability of any connection between two nodes, and the transmission delay of IoT devices working together in a distributed network. With the presented mathematical model, a routing table is maintained that let an individual node in a network find the distinctly prominent reliable path among many routes from source to destination. The superior efficiency of Qij routing scheme over eminent stochastic routing schemes is proven through simulation results in terms of reduced end-to-end expected delivery delay and increased expected delivery ratio.

关键词： Internet of things

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STRONGHOLD: Fast and Affordable Billion-Scale Deep Learning Model Training

STRONGHOLD: Fast and Affordable Billion-Scale Deep Learning ...

引用

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

作者： Sun, Xiaoyang Wang, Wei Qiu, Shenghao Yang, Renyu Huang, Songfang Xu, Jie Wang, Zheng Univ Leeds Leeds England Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665454445

Deep neural networks (DNNs) with billion-scale parameters have demonstrated impressive performance in solving many tasks. Unfortunately, training a billion-scale DNN is out of the reach of many data scientists because it requires high-performance GPU servers that are too expensive to purchase and maintain. We present STRONGHOLD, a novel approach for enabling large DNN model training with no change to the user code. STRONGHOLD scales up the largest trainable model size by dynamically offloading data to the CPU RAM and enabling the use of secondary storage. It automatically determines the minimum amount of data to be kept in the GPU memory to minimize GPU memory usage. Compared to state-of-the-art offloading-based solutions, STRONGHOLD improves the trainable model size by 1.9x similar to 6.5x on a 32GB V100 GPU, with 1.2x similar to 3.7x improvement on the training throughput. It has been deployed into production to successfully support large-scale DNN training.

关键词： Deep learning distributed training DNNs training acceleration

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DeepSpeed-Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale

DeepSpeed-Inference: Enabling Efficient Inference of Transfo...

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

作者： Aminabadi, Reza Yazdani Rajbhandari, Samyam Awan, Ammar Ahmad Li, Cheng Li, Du Zheng, Elton Ruwase, Olatunji Smith, Shaden Zhang, Minjia Rasley, Jeff He, Yuxiong Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781665454445

The landscape of transformer model inference is increasingly diverse in model size, model characteristics, latency and throughput requirements, hardware requirements, etc. With such diversity, designing a versatile inference system is challenging. DeepSpeed-Inference addresses these challenges by (1) a multiGPU inference solution to minimize latency while maximizing throughput for both dense and sparse transformers when the model fits in aggregate GPU memory, and (2) a heterogeneous inference solution that leverages CPU/NVMe/GPU memory to enable high-throughput inference for models larger than aggregate GPU memory. DeepSpeed-Inference reduces latency by 6.4x and increases throughput by 1.5x over the state-of-the-art. It enables trillion parameter scale inference under real-time latency constraints by leveraging hundreds of GPUs, an unprecedented scale for inference. It can inference 25x larger models than with GPU-only solutions, while delivering a high throughput of 84 TFLOPS (over 50% of A6000 peak).

关键词： Deep Learning distributed Inference Mixture of Experts PyTorch DeepSpeed Transformer models

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DEFT: distributed, Elastic, and Fault-tolerant State Management of Network Functions 19

DEFT: Distributed, Elastic, and Fault-tolerant State Managem...

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19th international conference on Network and Service Management (CNSM) - Network and Service Management in the Era of Generative AI and Digital Twins

作者： Shahriyar, Md Mahir Saha, Gourab Bhattacharjee, Bishwajit Reaz, Rezwana Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9783903176591

Network function virtualization is the key to developing elastically scalable and fault-tolerant network functions (e.g. load balancer, firewall etc.). By integrating NFV and SDN technologies, it is feasible to dynamically reroute traffic to new network function (NF) instances in the event of an NF failure or overload scenario. The fact that the majority of network functions are stateful makes the task more challenging. State migration and state replication are common approaches in achieving elasticity and fault tolerance. The majority of the studies in the literature either emphasize fault tolerance or elastic scalability while designing a state management system for network functions. In this paper, we have designed a complete state management system, called DEFT, that meets both elasticity and fault-tolerance goals. Our system also supports strong consistency on global state updates. While existing designs rely on a central controller or remote central storage to achieve strong consistency on state updates, DEFT utilizes distributed consensus mechanism to achieve the same. We have done a proof of concept implementation of DEFT and extensively tested DEFT under several model conditions to evaluate its scalability and performance. Our experimental results show that DEFT is scalable and maintains a considerably high throughput throughout. It incurs minimal performance overhead while achieving strong consistency on state updates.

关键词： Network function virtualization Software defined networking distributed computing Fault tolerant systems

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

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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： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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Comparative Analysis of Multi-Model and Uni-Model Approaches using Time distributed Bidirectional LSTM for Multidata Sentiment Analysis 14

Comparative Analysis of Multi-Model and Uni-Model Approaches...

引用

14th international conference on computing Communication and networking Technologies, ICCCNT 2023

作者： Gupta, Krati Parmar, Mahesh Madhav Institute of Technology & Science Dept. of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350335095

The sentiment of the data contained in a text can be determined by analysis. This sentiment can be good, negative, or neutral. When carrying out this research, one may choose to employ either a uni-model or a multi-model technique. In the uni-model method, both the training and the analysis are performed using just a single model. The application of this strategy is uncomplicated and uncomplicated, but it might not be able to make sense of complicated or different facts, which would lead to wrong findings. Instead, the multi-model technique utilizes a number of different models, each of which is designed to examine a particular aspect of the data or a particular kind of attitude. This allows the multi-model strategy to produce more accurate results. The results of several different models might be combined in order to obtain a more in-depth understanding of feelings. This method is more accurate overall and captures more complex expressions of emotion, but it requires more time and money to train personnel and put into practice. The evaluation of metrics was carried out using Multimodal and mono Model with Time distributed Bidirectional LSTM in this research. Both the loss and the accuracy. The Multi-Model achieves the highest accuracy of 99.71 percent for the train, while the validation accuracy achieves 99.32 percent, and the Multi-Model achieves the least loss of 0.010 percent. © 2023 IEEE.

关键词： Sentiment analysis

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Evaluating the Effectiveness of Mobile Device Security in Wireless Networks 3

Evaluating the Effectiveness of Mobile Device Security in Wi...

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3rd international conference on Smart Generation computing, Communication and networking, SMART GENCON 2023

作者： Pandey, Sakshi Naval, Preeti Varalakshmi, S. Pareek, Ravi Kant Renuka, G. Laxmi, A. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Center for Management Studies Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

this paper looks on the effectiveness of cell tool protection for Wi-Fi networks. It examines the technology and strategies used to defend records and communications even as on the pass, in addition to the significance of having a strong security machine in area. The usage of case research, the one of a kind features of mobile tool security are reviewed, such as encryption, authentication, and intrusion detection. The paper also explores the safety challenges posed through distributed networks and mobile users. Eventually, suggestions are made for making sure at ease mobility by using the usage of mobile safety technology and imposing protection policies. The goal is to provide an knowledge of the key elements of mobile safety so that you can help corporations expand secure Wi-Fi networks and enable their customers to be more comfy and efficient. © 2023 IEEE.

关键词： corporations distributed efficient elements technology

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SDADS: Stream Data Anomaly Detection System 2

SDADS: Stream Data Anomaly Detection System

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2nd international conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2023

作者： Wang, Yusheng Zhang, Anyi Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350331448

In a cloud-native architecture, the operational data of various system components experiences a significant increase. From the distributed complex system, obtaining the operation status data and realizing real-time monitoring and abnormal alarm play an important role in guaranteeing the smooth production. However, handling a large volume of stream data in real-time poses challenges such as high computational demands, low latency, and high concurrency. Therefore, this study presents the design and implementation of a system for anomaly detection in stream data called Stream Data Anomaly Detection System (SDADS). SDADS leverages message queues for processing business data and further processes the data using a distributed computing framework. It also provides functions for persistent data storage and anomaly detection alerts. The use of SDADS enables the management of complex business processes such as server provisioning and application deployment, simplifies business development logic, reduces operational time cost, and allows users to focus solely on the anomaly detection algorithms themselves. © 2023 IEEE.

关键词： Anomaly detection

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AI-Driven Symptom Checker: Leveraging Big Data Technologies for Real-Time Healthcare Triage and Diagnostics 5

AI-Driven Symptom Checker: Leveraging Big Data Technologies ...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Banerjee, Augnik Baibhav, Piyush Kumar, Nitin Singh, Aniket Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The healthcare industry continues to face significant challenges in delivering timely and accurate diagnoses, particularly in remote and underserved areas where access to medical professionals is limited. The work aims to develop an AI-driven symptom checker that leverages big data technologies to offer preliminary diagnoses and support patient triage, ensuring quicker medical response times. Utilizing Hadoop for scalable data storage, Hive for advanced data analysis, and Spark for real-time distributed data processing, the system is designed to efficiently handle large volumes of patient data. Tableau is integrated for intuitive data visualization, allowing healthcare professionals to gain actionable insights and make informed decisions promptly. The proposed architecture enables the swift analysis of patient symptoms in real-time, significantly reducing diagnostic delays and enhancing the quality of care. By focusing on scalable and efficient data processing, the work addresses the need for a robust healthcare solution capable of operating in environments with limited resources. This system has the potential to transform patient care in remote areas by providing real-time analysis, identifying critical cases, and aiding in effective resource allocation. The solution's emphasis on rapid diagnosis and visualization offers a comprehensive approach to improving healthcare outcomes in underserved regions. © 2024 IEEE.

关键词： AI-Driven Symptom Checker Apache Spark Hadoop Healthcare Triage Hive Tableau

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