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检索条件"丛书名=Computer Communications and Networks"

共 4934 条记录，以下是31-40 订阅

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Towards Optimal Load Balancing in Multi-Zone Kubernetes Clusters via Reinforcement Learning 33

Towards Optimal Load Balancing in Multi-Zone Kubernetes Clus...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Santos, Jose Wauters, Tim De Turck, Filip Steenkiste, Peter Ghent Univ Imec Dept Informat Technol IDLab Ghent Belgium Carnegie Mellon Univ Dept Comp Sci & Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9798350348439;9798350384611

With the advent of container technology, companies have been developing microservice-based applications, converting the old monolithic software into a group of loosely coupled containers, with the aim of offering greater flexibility and improving operational efficiency. When users access microservices, their initial point of contact is typically a load balancer. This component is responsible for distributing incoming traffic or requests between multiple instances of microservices. Traditional load balancing approaches mainly rely on round-robin, or weighted round-robin algorithms which are inadequate to maintain the overall performance and scalability of microservice-based applications. Microservices are often deployed in dynamic environments needing a more adaptive and efficient load balancing strategy to optimize resources and reduce the overall latency for end users. This paper presents a dynamic load balancer for Kubernetes (K8s) clusters based on Reinforcement Learning (RL). It aims to minimize the overall latency while promoting fair distribution of requests. To achieve this goal, the load balancer considers both current network delays and processing loads in the cluster. The evaluation shows that our solution is effective even in environments where both the network traffic and the processing loads in the cluster change dynamically over time. In addition, this study highlights the flexibility of DeepSets neural networks in solving the load balancing challenge in diverse setups without retraining. The results show that the DeepSets algorithms can solve the microservice load balancing problem even in scenarios up to 30 times larger than the trained setup.

关键词： Load Balancing Reinforcement Learning Microservices Kubernetes Containers

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Learning-Based WiFi Fingerprint Inpainting via Generative Adversarial networks 33

Learning-Based WiFi Fingerprint Inpainting via Generative Ad...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Chan, Yu Lin, Pin-Yu Tseng, Yu-Yun Chen, Jen-Jee Tseng, Yu-Chee Natl Yang Ming Chiao Tung Univ Coll Artificial Intelligence Tainan Taiwan

ISBN: (纸本)9798350348439;9798350384611

WiFi-based indoor positioning has been extensively studied. A fundamental issue in such solutions is the collection of WiFi fingerprints. However, due to real-world constraints, collecting complete fingerprints at all intended locations is sometimes prohibited. This work considers the WiFi fingerprint inpainting problem. This problem differs from typical image/video inpainting problems in several aspects. Unlike RGB images, WiFi field maps come in any shape, and signal data may follow certain distributions. Therefore, it is difficult to forcefully fit them into a fixed-dimensional matrix, as done with processing images in RGB format. As soon as a map is changed, it also becomes difficult to adapt it to the same model due to scale issues. Furthermore, such models are significantly constrained in situations requiring outward inpainting. Fortunately, the spatial relationships of WiFi signals and the rich information provided among channels offer ample opportunities for this generative model to accomplish inpainting. Therefore, we designed this model to not only retain the characteristic of regression models in generating fingerprints of arbitrary shapes but also to accommodate the observational outcomes from densely deployed APs. This work makes two major contributions. Firstly, we delineate the distinctions between this problem and image inpainting, highlighting potential avenues for research. Secondly, we introduce novel generative inpainting models aimed at capturing both inter-AP and intra-AP correlations while preserving latent information. Additionally, we incorporate a specially designed adversarial discriminator to enhance the quality of inpainting outcomes.

关键词： adversarial network indoor localization in-painting WiFi fingerprint wireless sensing

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ViEdge: An Edge-based Platform for Video Analytics Applications in Smart Estates 33

ViEdge: An Edge-based Platform for Video Analytics Applicati...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Choudhary, Vishal Aggarwal, Rahul Lim, Hock Beng Chen, Binbin Singapore Univ Technol & Design Ctr Smart Syst CS2 Singapore Singapore Singapore Univ Technol & Design Informat Syst Technol & Design Pillar ISTD Singapore Singapore

ISBN: (纸本)9798350348439;9798350384611

Video analytics plays a crucial role in the development of smart estates and cities. Applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast response time. Traditional cloud-based systems are ill-suited for these applications due to their limitations in handling large volume of video data with low latency. In contrast, the IoT-Edge-Cloud paradigm is better suited for such applications, but it presents challenges such as system heterogeneity, and resource allocation and orchestration. There is a need for an efficient platform for distributed video stream processing where resource orchestration and scalability aspects are tailored to smart estate applications. In this paper, we present ViEdge, an edge-based platform for video analytics applications in smart estates. It is highly adaptable and scalable, making it ideal for various deployments in such environments. Our implementation of ViEdge utilizes Kubernetes (K8s) for resource management and orchestration, and Apache Storm for distributed video stream processing. To study ViEdge's customization capabilities, we evaluated its performance on a heterogeneous edge testbed. We observed increased latency in Apache Storm when integrated with Kubernetes, affecting overall application performance. However, by developing a heuristic-based scheduler, we demonstrate that ViEdge effectively reduces end-to-end latency and enhances frame processing rates.

关键词： Edge Computing Video Analytics Distributed Video Stream Processing Kubernetes Apache Storm

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Beyond Traditional Methods: Deep Learning with Data Augmentation for Robust Access Control 33

Beyond Traditional Methods: Deep Learning with Data Augmenta...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Al Lail, Mustafa Pinto, Daniela Almanza, Luis Alvarez Salazar, Francisco Rizzi, Carolina Texas A&M Int Univ Sch Engn Laredo TX 78041 USA

ISBN: (纸本)9798350348439;9798350384611

Access control systems in large organizations often struggle with managing complex policies and workloads. However, there is potential for deep learning models to address these challenges. This study delves into the suitability of various deep learning architectures for making real-time access control decisions. Six prominent Convolutional Neural Network (CNN) models (ResNet, DenseNet, Xception, Inception, AlexNet, VGG-16) are evaluated, and the impact of data augmentation using SMOTE on their performance is analyzed. The findings demonstrate that most deep learning models consistently deliver results in access control. ResNet outperforms other models, showing high accuracy across original and SMOTE-augmented datasets. Moreover, SMOTE generally enhances performance for most models, highlighting its potential for addressing data imbalance. These results indicate that deep learning shows promise for improving access control tasks.

关键词： Access control Deep Learning CNN Security

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Multimodal Frequeny Spectrum Fusion Schema for RGB-T Image Semantic Segmentation 33

Multimodal Frequeny Spectrum Fusion Schema for RGB-T Image S...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Liu, Hengyan Zhang, Wenzhang Dai, Tianhong Yin, Longfei Ren, Guangyu Xian Jiaotong Liverpool Univ Sch AI & Adv Comp Suzhou Peoples R China Xian Jiaotong Liverpool Univ Sch Internet Things Suzhou Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen Scotland Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9798350348439;9798350384611

Semantic segmentation confronts challenges with traditional networks tailored exclusively for RGB inputs, which may suffer from quality degradation under adverse conditions like low-level illumination or inclement weather. Recent advancements have shown promising outcomes by integrating RGB images with corresponding thermal infrared (TIR) images. However, effectively fusing features from both modalities remains a significant challenge. In this paper, we introduce a novel approach termed Multimodal Frequency Spectrum Fusion Schema (MFSFS) for semantic segmentation of RGB-T images. MFSFS leverages the advantages of the frequency spectrum to effectively extract and utilize multimodal feature information. To mitigate redundant information's adverse effects during multimodal fusion in the frequency domain, we propose a diversity-oriented contrastive learning approach. Simulation results demonstrate that MFSFS achieves competitive performance while maintaining a relatively smaller model size.

关键词： Semantic Segmentation Multimodal Fusion Frequency Spectrum Contrastive Learning Determinantal point processes

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Appro-Fun: Approximate Machine Unlearning in Federated Setting 33

Appro-Fun: Approximate Machine Unlearning in Federated Setti...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Xiong, Zuobin Li, Wei Cai, Zhipeng Univ Nevada Dept Comp Sci Las Vegas NV 89154 USA Georgia State Univ Dept Comp Sci Atlanta GA USA

ISBN: (纸本)9798350348439;9798350384611

Machine learning models contain much information about the training dataset, so even if some data points are deleted, the private information can still be inferred. To counteract this problem, "machine unlearning", as an emerging data management approach, is proposed to remove data from the databases and the influence of data from the trained models. Such a technique is vital in the current era of data-driven applications, where the privacy and security of users can be guaranteed. Yet, machine unlearning is still in its early stage, and there are rare existing methods for machine unlearning in the federated setting that is a more practical and crucial scenario. Therefore, this paper investigates the federated machine unlearning problem where the local clients of a federated system intend to delete their local private data appropriately. The proposed method is termed Approximate Federated unlearning (Appro-Fun), which adopts differential privacy and second-order optimization to achieve (epsilon, delta)-approximate unlearning on trained models. Rigorous theoretic analysis presents the performance guarantee of Appro-Fun, and real-data experiments validate the advantages of Appro-Fun compared with the state-of-the-art.

关键词： federated learning machine unlearning privacy and security data management

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Agent Deprogramming Finetuning Away Backdoor Triggers for Secure Machine Learning Models/LLMs 33

Agent Deprogramming Finetuning Away Backdoor Triggers for Se...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Edwards, Demarcus Rawat, Danda B. Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA

ISBN: (纸本)9798350348439;9798350384611

Adversarial fine-tuning has shown promise in improving model robustness against test-time perturbations, but its effectiveness in mitigating backdoor attacks in language models remains understudied. This work investigates the use of adversarial fine-tuning techniques and deprogramming, specifically focusing on basic fine-tuning, fine-tuning with paraphrasing, and fine-tuning with retokenization, to mitigate backdoor attacks in language models. We evaluate the effectiveness of these techniques by measuring the accuracy of our models with and without the context of the original query. Our approach extends the idea of supervised fine-tuning (SFT) and incorporates adaptors from the parameter-efficient fine-tuning (PEFT) framework to efficiently fine-tune our models while minimizing computational resources. While supervised fine-tuning alone can alleviate backdoor triggers to some extent, our results demonstrate that combining SFT with paraphrasing and retokenization techniques can achieve even more effective mitigation. In particular, adversarial fine-tuning with paraphrasing significantly reduces the success rate of backdoor attacks in language models.

关键词： AI Agent Deprogramming Adversarial Learning Adversarial Examples Mitigating backdoor attacks in AI Fine tuning AI models Backdoor Triggers in Language Models LLMs Large Language Models

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Traffic Engineering in Large-scale networks via Multi-Agent Deep Reinforcement Learning with Joint-Training 33

Traffic Engineering in Large-scale Networks via Multi-Agent ...

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Van An Le Duc Long Nguyen Phi Le Nguyen Ji, Yusheng Natl Inst Adv Ind Sci & Technol Tokyo Japan Hanoi Univ Sci & Technol Hanoi Vietnam Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350348439;9798350384611

Reinforcement learning (RL) has been successfully applied in many fields for building autonomous systems, such as robotics and telecommunications. With its self-learning ability, RL provides a framework for learning from historical experience and adapting to dynamic environments. In response to the surge in network traffic and the evolving nature of traffic behavior, RL has emerged as a crucial technique for developing intelligent and adaptive traffic engineering (TE) solutions. However, most prior studies have focused on using a centralized unit (i.e., a single agent) to construct RL-based TE systems. While the centralized approach leverages global network information for solid performance, it encounters challenges related to scalability, dynamic network topology, and high monitoring overhead for collecting network information. This paper addresses these issues by introducing a jointly trained multi-agent reinforcement learning-based traffic engineering (MATE-JT) system, which operates as a distributed TE solution. Our approach utilizes multiple agents within a network node so that each agent can make independent routing decisions for a subset of flows. We take the approach of sharing parameters among agents and introduce a joint-training technique that facilitates simultaneous learning from multiple agents' experiences. As a result, our proposed method enhances system performance while reducing training time. We evaluate the proposed approach using various network traffic datasets and demonstrate that MATE-JT improves the performance of TE (about 6.5%) and achieves faster convergence (about 35%) in large-scale networks when compared to state-of-the-art methods.

关键词： Multi-agent Deep Reinforcement Learning Traffic Engineering Large-scale networks

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Implementation and Performance Measurement of the SUHDSA(Secure, Useful, and High-performance Data-Stream Anonymization) plus Algorithm 33

Implementation and Performance Measurement of the SUHDSA(Sec...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Lee, Jiyeon Kim, Soonseok Halla Univ Dept AI Informat Secur Wonju South Korea

ISBN: (纸本)9798350348439;9798350384611

This paper focuses on tables in microdata format, where each tuple contains specific individual information collected in real-time and stored at the tuple level. The collected information within each tuple, being personal, necessitates privacy protection. Therefore, we need to anonymize this information, which is the main theme of this paper. In this context, we have proposed the SUHDSA algorithm, an improvement over Sopaoglu and Abul's UBDSA algorithm, and have further enhanced its security with the proposed SUHDSA+ algorithm. This paper aims to validate the results of our previously proposed SUHDSA+ algorithm through empirical experiments. The experiments show that while the existing SUHDSA+ takes between 3.94 to 7.98 seconds longer in execution time compared to the SUHDSA algorithm, it performs about 45.6% faster than the original UBDSA. In terms of information loss, there was an increase of up to 18% compared to the original SUHDSA;however, when the k-value increased from 3 to 10, the loss rate was up to 19% less, indicating a significant impact of the increased k-value.

关键词： anonymization privacy real-time microdata

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Usage-Dependent Quality of Service for Free WiFi networks 33

Usage-Dependent Quality of Service for Free WiFi Networks

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33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Laidig, Robin Herrmann, Jona Duerr, Frank Rothermel, Kurt Univ Stuttgart IPVS Stuttgart Germany

ISBN: (纸本)9798350348439;9798350384611

Free WiFi services offer Internet access at many public places like airports or in public transport vehicles. However, the experienced Quality of Service is often poor and unequal between users. While some users can run bandwidth-intensive applications such as video streaming in these networks, others cannot even perform simple web browsing. The reason for this are extensive delays, caused by network congestion of bandwidth-intensive applications, which especially impair interactive applications such as web browsing. In this paper, we propose a novel approach to improve fairness and user experience when sharing common network resources. To this end, we present a Usage-Dependent Quality of Service model (UD-QoS) that dynamically prioritizes network traffic based on the usage intensity of each participant. It shields participants with low usage intensity from congestion delays caused by participants who use the service extensively. We describe the versatile configuration possibilities of UD-QoS and present a Linux-based proof-of-concept implementation. Our evaluations with a physical testbed and popular real-world applications such as video streaming, social media and web browsing show a significant reduction of response times by up to 79% with UD-QoS while keeping processing overhead low. The benefits are particularly large for participants with low usage intensity.

关键词： Quality of Service QoS model Public WiFi Free WiFi Congestion Avoidance Active Queue Management

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