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Network Anomaly Detection in distributed Edge computing Infrastructure

Network Anomaly Detection in Distributed Edge Computing Infr...

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Consumer Communications and networking conference, CCNC IEEE

作者： William Marfo Enrique A. Rico Deepak K. Tosh Shirley V. Moore Department of Computer Science University of Texas at El Paso El Paso USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests $(p<0.05)$ .

关键词： Training Fault tolerance Accuracy Federated learning Roads Scalability Computational modeling Fault tolerant systems Anomaly detection Edge computing

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Balancing computing and networking in Autonomous Edge Clouds

Balancing Computing and Networking in Autonomous Edge Clouds

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Consumer Communications and networking conference, CCNC IEEE

作者： Shinnosuke Masuda Tsuyoshi Hasegawa Kazuyuki Shudo Kenjiro Cho Kyoto University IIJ

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

In the ever-changing environment of edge cloud systems, it is necessary to allocate diverse cloud resources in an efficient manner, at the same time, reducing energy consumption. A simple distributed resource allocation model is proposed in a previous study that utilizes a pseudo cost function of resource load, and it is shown that a convex function allows energy saving by pooling idle edge servers. In this paper, we extend this cost model beyond simple load, and present a method to balance computation and communication by merging multiple constraints into the cost function. We show trade-offs among soft constraints along with energy saving effects by simulation, using specific scenarios with computational and communication constraints. Our simulation results show that the combination of a convex function with a polynomial penalty function works well for balancing energy saving and other soft constraints.

关键词： Computational modeling Simulation Energy conservation Cost function Convex functions Polynomials Resource management Servers Load modeling Edge computing

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One-shot Bottleneck Size Optimization in CNN for Split computing

One-shot Bottleneck Size Optimization in CNN for Split Compu...

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Consumer Communications and networking conference, CCNC IEEE

作者： Yutaro Horikawa Takayuki Nishio School of Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Split computing (SC) is a distributed inference method designed to balance computational load and reduce latency by partitioning a neural network into head and tail networks, deployed on a mobile device and a server, respectively. Due to the limited bandwidth, the data size at the split point must be reduced. For this reason, layers with small channel sizes, called bottlenecks, are often introduced in the model. However, the degree to which the channel size should be reduced depends on the overall structure of the model and the difficulty of the task, so a search is necessary. In this paper, we propose a training method that simultaneously reduces the number of bottleneck channels and optimizes model parameters for SC. This method introduces weights for the bottleneck layer into the loss function of training using cross-entropy. This makes the output value of each channel in the bottleneck layer closer to zero, and reduces the size of the transmitted data by filling in the values close to zero at the server side instead of sending them by the client during inference.

关键词： Training Accuracy Neural networks Tail Machine learning Mobile handsets Filling Computational efficiency Servers Optimization

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Improving Semi-Supervised Federated Learning with Limited Labeled Data via Adaptive Batchsize and Pseudo Labeling

Improving Semi-Supervised Federated Learning with Limited La...

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Consumer Communications and networking conference, CCNC IEEE

作者： Byoungjun Park Pedro Porto Buarque de Gusmão Minhoe Kim Dept. of Computer and Information Science Korea University Sejong South Korea Computer Science Research Centre University of Surrey Guildford United Kingdom

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Federated learning (FL) is a distributed learning method that leverages numerous edge devices for training while protecting data privacy. However, most of the data produced by distributed edge devices are unlabeled, leading to the emergence of semi-supervised federated learning (SSFL) to address this issue. Although state-of-the-art approaches perform well when labeled data is sufficient, it shows severe performance degradation and training becomes more challenging as labeled data becomes scarce. In this paper, we improve performance in environments with limited labeled data by dynamically adjusting batch sizes and applying an Adaptive Threshold (AT). Additionally, we propose methods to resolve issues arising when adopting Adaptive Thresholds method of the centralized approach and investigate their limitations.

关键词： Performance evaluation Training Degradation Multi-access edge computing Federated learning Distance learning distributed databases Semisupervised learning Servers Labeling

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Heuristic Data Placement in distributed Hierarchical Storage Network Using Deep Reinforcement Learning

Heuristic Data Placement in Distributed Hierarchical Storage...

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Consumer Communications and networking conference, CCNC IEEE

作者： Ravi Kaler Durga Toshniwal Computer Science and Engineering Department Indian Institute of Technology Roorkee Uttarakhand India

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Intelligent data placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient data placement can lead to significant delays in data movement and increased service latency, impacting the overall performance of a distributed environment. It is a complex online decision-making problem due to its varying access patterns, dynamic network conditions, and ever-changing distributed environment, which traditional network optimization techniques fail to address efficiently. Reinforcement learning is best suited to address this problem as it learns from the environment dynamically and performs action. This paper presents a novel deep reinforcement learning (DRL) framework, soft actor-critic-2 (SAC-2), for heuristic data placement in the distributed network, leveraging the state-of-the-art SAC technique to improve efficiency, reduce latency, and subsequently minimize storage costs. We formulate the problem as a Markov Decision Process (MDP) model and incorporate a prioritized replay buffer for efficient learning. The heuristic data placement technique improves distributed hierarchical storage system efficiency compared to baseline strategies, as demonstrated by extensive experiments on Microsoft Research Cambridge (MSR) network traces.

关键词： Performance evaluation Cloud computing Costs Markov decision processes Decision making distributed databases Deep reinforcement learning Delays Time factors Optimization

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Conditional Recurrent Neural Networks for Enhancing Throughput Prediction and Slow File Transfers Detection in Large Science Workflows

Conditional Recurrent Neural Networks for Enhancing Throughp...

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Consumer Communications and networking conference, CCNC IEEE

作者： Boyu Fan Alex Sim Kesheng Wu Jinoh Kim University of California Berkeley Lawrence Berkeley National Laboratory Energy Sciences Network Texas A&M University-Commerce

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Efficient data transfer across scientific computing facilities is critical for enabling timely scientific discoveries. In this work, we explore the options of anticipating extremely slow data transfers to enable preventive actions. However, the dynamic nature of the large distributed scientific workflows driving these data transfers presents significant challenges for predicting network throughput. This study introduces a Conditional Recurrent Neural Network (CondRNN) model, specifically utilizing Conditional Long Short-Term Memory (CondLSTM), to integrate both static and dynamic features for enhanced throughput prediction. By leveraging historical transfers as proxy features, more than 60% of predictions achieved an absolute percentage error (APE) of less than 20%, and slow transfers were detected with a precision of 91.7% and recall of 100%, outperforming traditional RNN models. Implementing CondLSTM in scientific computing environments can optimize network resource utilization, ensuring efficient data transmission, thereby supporting the continuous progression of scientific research.

关键词： Recurrent neural networks Accuracy Scientific computing Computational modeling Predictive models Throughput Data transfer Data models Resource management Long short term memory

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TUNE-FL: Adaptive Semi-Synchronous Semi-Decentralized Federated Learning

TUNE-FL: Adaptive Semi-Synchronous Semi-Decentralized Federa...

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Consumer Communications and networking conference, CCNC IEEE

作者： Houssem Jmal Kandaraj Piamrat Ons Aouedi École Centrale Nantes IMT Atlantique CNRS INRIA LS2N UMR Nantes University Nantes France SnT SIGCOM University of Luxembourg Luxembourg

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Today, Federated Learning (FL) stands out as the solution to addressing the challenges of distributed computing and empowering a wide range of edge devices with artificial intelligence capabilities. One variant of FL called semi-decentralized FL (SDFL) enables multiple server units to coordinate the learning task instead of relying on only one central server, hence preventing single-point failures. However, SDFL requires careful consideration regarding the coordination between server nodes, and dealing with the heterogeneous computing resources and data distributions across end devices (FL clients). Therefore, we propose TUNE-FL, an adapTive semi-synchronoUs semi-deceNtralizEd Federated Learning that addresses the clients' heterogeneity challenges. TUNE-FL alleviates these challenges by (i) ensuring consensus regardless of the network topology, and (ii) deploying an adaptive semi-synchronous mechanism for coordinating the learning process across all nodes while taking into consideration the heterogeneity presented by end devices. We evaluated TUNE-FL for the intrusion detection system (IDS) datasets and compared it with the three most representative baseline models. The experimental results demonstrate that TUNE-FL outperforms the baselines in accuracy while greatly reducing the duration of FL training by approximately 97 times.

关键词： Training Adaptive systems Federated learning Network topology Image edge detection Intrusion detection Heterogeneous networks Servers Synchronization distributed computing

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Energy-Aware Clustering for Efficient CKKS Encryption in Smart Agriculture

Energy-Aware Clustering for Efficient CKKS Encryption in Sma...

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Consumer Communications and networking conference, CCNC IEEE

作者： Huzaif Khan Lokesh Purohit Ali Jalooli Department of Computer Science California State University Dominguez Hills Carson CA USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Securing data communication in smart agriculture is crucial to ensure the integrity and confidentiality of information transmitted from distributed sensor networks to cloud infrastructure. Cheon-Kim-Kim-Song (CKKS) homomorphic encryption offers robust security by enabling computations on encrypted data. However, its high computational and energy demands pose challenges for battery-powered sensors. To address this, we propose the EnergyGreedy Clustering Algorithm (EGCA), which reduces energy consumption by optimizing sensor clustering. Each cluster is managed by a cluster head that aggregates data and minimizes direct transmissions, making CKKS encryption more feasible in resource-constrained environments. Our simulations show that EGCA significantly improves packet delivery ratio and delay to provide a robust solution for secure data communication in smart agriculture.

关键词： Smart agriculture Energy consumption Cloud computing Clustering algorithms Data aggregation Energy efficiency Data models Delays Data communication Homomorphic encryption

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distributed and Parallel Low-Diameter Decompositions for Arbitrary and Restricted Graphs 16

Distributed and Parallel Low-Diameter Decompositions for Arb...

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16th Innovations in Theoretical Computer Science conference, ITCS 2025

作者： Dou, Jinfeng Götte, Thorsten Hillebrandt, Henning Scheideler, Christian Werthmann, Julian Paderborn University Germany University of Hamburg Germany

ISBN: (纸本)9783959773614

We consider the distributed and parallel construction of low-diameter decompositions with strong diameter. We present algorithms for arbitrary undirected, weighted graphs and also for undirected, weighted graphs that can be separated through k ∈ Õ(1) shortest paths. This class of graphs includes planar graphs, graphs of bounded treewidth, and graphs that exclude a fixed minor Kr. Our algorithms work in the PRAM, CONGEST, and the novel HYBRID communication model and are competitive in all relevant parameters. Given D > 0, our low-diameter decomposition algorithm divides the graph into connected clusters of strong diameter D. For an arbitrary graph, an edge e ∈ E of length e is cut between two clusters with probability O(e·log(n)D). If the graph can be separated by k ∈ Õ(1) paths, the probability improves to O(e·log log nd). In either case, the decompositions can be computed in Õ(1)1 depth and Õ(m) work in the PRAM and Õ(1) time in the HYBRID model. In CONGEST, the runtimes are Õ(HD + √n) and Õ(HD) respectively. All these results hold w.h.p.2 Broadly speaking, we present distributed and parallel implementations of sequential divide-and-conquer algorithms where we replace exact shortest paths with approximate shortest paths. In contrast to exact paths, these can be efficiently computed in the distributed and parallel setting [STOC’22]. Further, and perhaps more importantly, we show that instead of explicitly computing vertex-separators to enable efficient parallelization of these algorithms, it suffices to sample a few random paths of bounded length and the nodes close to them. Thereby, we do not require complex embeddings whose implementation is unknown in the distributed and parallel setting. © Jinfeng Dou, Thorsten Götte, Henning Hillebrandt, Christian Scheideler, and Julian Werthmann.

关键词： Undirected graphs

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Quality of Control Based Resource Dimensioning for Collaborative Edge Robotics 22

Quality of Control Based Resource Dimensioning for Collabora...

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Roy, Neelabhro Dhullipalla, Mani H. Sharma, Gourav Prateek Dimarogonas, Dimos V. Gross, James School of Electrical Engineering and Computer Science Digital Futures Research Center Kth Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9798331508050

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably. © 2025 IEEE.

关键词： collaborative robotics edge computing multi-agent systems quality of control safety-critical applications

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