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HTKT: Knowledge Tracing Based on Hypergraph Transformer 22

HTKT: Knowledge Tracing Based on Hypergraph Transformer

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22nd IEEE international Symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Guo, Xiaojin Chang, Xilong Zhang, Xiankun Liang, Kun Shang, Yuhu Zhang, Yiying Tianjin University of Science and Technology College of Artificial Intelligence Tianjin300457 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

ISBN: (纸本)9798331509712

Knowledge tracing(KT) is one of the supporting technologies for adaptive learning. It obtains learners' knowledge level by analyzing their online historical answer records, thereby predicting their future answer performance. Since there are a large number of continuous or repeated exercises in KT datasets, and traditional graph structures can usually only represent one-to-one or one-to-many relationships. Some existing models tend to ignore the potential connections between knowledge concept, and thus fail to accurately obtain high-level representations of students' knowledge state. Hypergraphs can directly represent many-to-many relationships and have advantages in extracting high-level information and processing complex relationships. In this paper, we propose a KT method (HTKT) based on Hypergraph Transformer. This method is based on the Encoder-only architecture and introduces a hypergraph attention mechanism to obtain a high-order feature representation of students' knowledge state. Experiments on multiple public and general datasets show that our proposed HTKT method outperforms existing baseline methods. © 2024 IEEE.

关键词： Students

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Orchestration of heterogeneous, distributed, real-time control containers 22

Orchestration of heterogeneous, distributed, real-time contr...

引用

22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Walker, Moritz Rupietta, Leonard Lechler, Armin Neubauer, Michael Verl, Alexander University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798331527471

In today’s global market, manufacturers must be highly adaptable, producing profitably smaller batches. Disintegrating hardware and software and embracing modern IT concepts like containerization and Service-oriented Architecture enable this flexibility. While offering rapid reconfiguration, these approaches add complexity. Orchestration tools aid in analyzing response times, workload mapping, and parameter selection for containerized real-time applications. However, current tools struggle with managing distributed, heterogeneous, real-time apps. This paper proposes an orchestration solution based on Compositional Performance Analysis and Genetic Algorithm to optimize task allocation across diverse computing resources. The approach enables the orchestration of heterogeneously scheduled, container-based, distributed real-time systems. © 2024 IEEE.

关键词： Service oriented architecture (SOA)

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Dynamic VNF Deployment and Resource Allocation in Mobile Edge computing 22

Dynamic VNF Deployment and Resource Allocation in Mobile Edg...

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22nd IEEE international Symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Zhai, Xiaolong He, Zhenli Xiao, Yuanfei Wu, Junjie Yu, Xuejie Yunnan University Yunnan Key Laboratory of Software Engineering Kunming China Yunnan University School of Software Kunming650504 China

ISBN: (纸本)9798331509712

The explosive growth of terminal devices at the network edge, coupled with advancements in communication technology, poses significant challenges to traditional cloud computing models. Despite Mobile Edge computing (MEC) mitigating some issues by enabling real-time data processing closer to the source, it faces challenges with linear growth in computational resources insufficient to meet the exponential growth in service demand. Existing research utilizing Software-Defined networking (SDN), Network Functions Virtualization (NFV), and Service Function Chain (SFC) technologies has made progress but still faces critical issues, such as the inability to respond in real-time to dynamic demands and inefficiencies in resource management strategies. This paper addresses these challenges by optimizing Virtual Network Function (VNF) deployment strategies in MEC environments. We propose PPO-ERA, a novel algorithm leveraging deep reinforcement learning and the Karush-Kuhn-Tucker (KKT) method. This approach provides real-time, adaptive, and dynamic deployment policies for VNFs, significantly improving both the average response delay of tasks and resource utilization. Key contributions include rigorous SFC-based application modeling, dynamic VNF deployment algorithms, elastic resource allocation, uniform state representation, and extensive performance validation. These advancements enhance the adaptability, efficiency, and performance of VNF deployment strategies, addressing critical challenges in dynamic MEC environments. © 2024 IEEE.

关键词： Network function virtualization

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Optimizing Service Selection and Load Balancing in Multi-Cluster Microservice Systems with MCOSS 22

Optimizing Service Selection and Load Balancing in Multi-Clu...

引用

22nd international-Federation-for-Information-Processing (IFIP) networking conference (IFIP networking)

作者： Bachar, Daniel Bremler-Barr, Anat Hay, David Reichman Univ Dept Comp Sci Herzliyya Israel Tel Aviv Univ Dept Comp Sci Tel Aviv Israel Hebrew Univ Jerusalem Dept Comp Sci Jerusalem Israel

ISBN: (纸本)9783903176577

With the advent of cloud and container technologies, enterprises develop applications using a microservices architecture, managed by orchestration systems (e.g. Kubernetes), that group the microservices into clusters. As the number of application setups across multiple clusters and different clouds is increasing, technologies that enable communication and service discovery between the clusters are emerging (mainly as part of the Cloud Native ecosystem). In such a multi-cluster setting, copies of the same microservice may be deployed in different geo-locations, each with different cost and latency penalties. Yet, current service selection and load balancing mechanisms do not take into account these locations and corresponding penalties. We present MCOSS, a novel solution for optimizing the service selection, given a certain microservice deployment among clouds and clusters in the system. Our solution is agnostic to the different multi-cluster networking layers, cloud vendors, and discovery mechanisms used by the operators. Our simulations show a reduction in outbound traffic cost by up to 72% and response time by up to 64%, compared to the currently-deployed service selection mechanisms.

关键词： Optimization Kubernetes Cloud computing Multi-Cloud Multi-Cluster Microservices Load Balancing

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22nd IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems, DAIS 2022 Held as part of the 17th international Federated conference on distributed computing Techniques, DisCoTec 2022

22nd IFIP WG 6.1 International Conference on Distributed App...

引用

22nd IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems, DAIS 2022 Held as part of the 17th international Federated conference on distributed computing Techniques, DisCoTec 2022

ISBN: (纸本)9783031160912

The proceedings contain 12 papers. The special focus in this conference is on distributed Applications and Interoperable Systems. The topics include: The HDFS Replica Placement Policies: A Comparative Experimental Investigation;an Elastic and Scalable Topic-Based Pub/Sub System Using Deep Reinforcement Learning;challenges in Automated Measurement of Pedestrian Dynamics;Using SGX for Meta-Transactions Support in Ethereum DApps;understanding Cryptocoins Trends Correlations;rebop: Reputation-Based Incentives in Committee-Based Blockchains;lesser Evil: Embracing Failure to Protect Overall System Availability;failure Root Cause Analysis for Microservices, Explained;attestation Mechanisms for Trusted Execution Environments Demystified;accelerate Model Parallel Deep Learning Training Using Effective Graph Traversal Order in Device Placement;analysis of the Impact of Interaction Patterns and IoT Protocols on Energy Consumption of IoT Consumer Applications.

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AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving 23

AutoFed: Heterogeneity-Aware Federated Multimodal Learning f...

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29th Annual international conference on Mobile computing and networking (ACM MobiCom)

作者： Zheng, Tianyue Li, Ang Chen, Zhe Wang, Hongbo Luo, Jun Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Fudan Univ Intelligent Networking & Comp Res Ctr Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781450399906

Object detection with on-board sensors (e.g., lidar, radar, and camera) is crucial to autonomous driving (AD), and these sensors complement each other in modalities. While crowdsensing may potentially exploit these sensors (of huge quantity) to derive more comprehensive knowledge, federated learning (FL) appears to be the necessary tool to reach this potential: it enables autonomous vehicles (AVs) to train machine learning models without explicitly sharing raw sensory data. However, the multimodal sensors introduce various data heterogeneity across distributed AVs (e.g., label quantity skews and varied modalities), posing critical challenges to effective FL. To this end, we present AutoFed as a heterogeneityaware FL framework to fully exploit multimodal sensory data on AVs and thus enable robust AD. Specifically, we first propose a novel model leveraging pseudo labeling to avoid mistakenly treating unlabeled objects as the background. We also propose an autoencoder-based data imputation method to fill missing data modality (of certain AVs) with the available ones. To further reconcile the heterogeneity, we finally present a client selection mechanism based on client model similarities to improve training stability and convergence rate. Our experiments confirm that AutoFed substantially improves over status quo in both precision and recall, while demonstrating strong robustness to adverse weather.

关键词： Autonomous driving autonomous vehicle object detection federated learning crowdsensing multimodal learning

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Design and Implementation of a distributed Firewall Management System for Improved Security 22

Design and Implementation of a Distributed Firewall Manageme...

引用

22nd RoEduNet conference: networking in Education and Research, RoEduNet 2023

作者： Tudosi, Andrei-Daniel Graur, Adrian Balan, Doru Gabriel Potorac, Alin Dan Stefan cel Mare University of Suceava Department of Computers Electronics and Automation Suceava Romania

ISBN: (纸本)9798350393651

Network security is the practice of protecting computer networks and devices from unauthorized access, attacks, and threats. These threats can come from a variety of sources, including hackers, malware, and external attacks, and they can compromise the confidentiality, integrity, and availability of sensitive data and systems. A network security management system is a system that is designed to help protect and secure a network from these threats. In this context, this article presents an approach to create a management system for a distributed firewall, which provides multiple benefits that will be presented in the following sections. The script obtains the CPU utilization and network bandwidth data of the WAN interface for each virtual machine and optimization algorithm, and then outputs the retrieved information. The application programming interface APD is used to access the data from the virtualization platform and to modify the optimization process on the firewall. In this way, the solution is capable to monitor the entire network and make decisions based on parameters to ensure a high-performance security. The proposed script manages virtual machines inside the virtualization platform using an API to retrieve data and change the optimization algorithm of the firewall. © 2023 IEEE.

关键词： Network security

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Decentralized Disaster Management in Smart Cities: Leveraging IPFS, Blockchain, and Edge computing 22

Decentralized Disaster Management in Smart Cities: Leveragin...

引用

22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Madhusudhan, R. Rithesh, S. National Institute of Technology Department of Mathematical and Computational Sciences Karnataka Surathkal India

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508050

As more and more smart cities come into existence, the necessity for foolproof disaster management solutions intensifies. This paper probes the challenges that smart cities face regarding disaster management and proposes a distributed framework leveraging a six-layer architecture involving Inter-Planetary File System (IPFS), blockchain technology, and edge computing to boost resilience and response capabilities. IPFS provides decentralized storage with interfacing, thus guaranteeing data integrity and availability during natural disasters. Blockchain technology helps in improving data transparency, ensuring tamper-proof updates, and elevating logistics through-put. Smart contracts facilitate efficient coordination and resource allocation. Edge computing enables real-time data processing, optimizing resource allocation, and enhancing the routing of physical resources to key locations for increased system efficiency. Connecting, investigating, aligning, and sharing information in a decentralized manner fosters teamwork among stakeholders. Community engagement and reporting tools are provided for smart city dwellers to participate in disaster management activities. The proposed system ensures a more resilient and faster disaster response. © 2025 IEEE.

关键词： Signaling

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SoK: The Evolution of distributed Dataset Synchronization Solutions in ndN 9

SoK: The Evolution of Distributed Dataset Synchronization So...

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22nd international conference on Information Centric networking

作者： Moll, Philipp Patil, Varun Wang, Lan Zhang, Lixia Univ Calif Los Angeles Los Angeles CA 90024 USA Univ Memphis Memphis TN USA

ISBN: (纸本)9781450392570

distributed dataset synchronization, or Sync in short, plays the role of a transport service in the Named Data networking (ndN) architecture. A number of ndN Sync protocols have been developed over the last decade. In this paper, we conduct a systematic examination of ndN Sync protocol designs, identify common design patterns, reveal insights behind different design approaches, and collect lessons learned over the years. We show that (i) each Sync protocol can be characterized by its design decisions on three basic components - dataset namespace representation, namespace encoding for sharing, and change notification mechanism, and (ii) two or three types of choices have been observed for each design component. Through analysis and experimental evaluation, we reveal how different design choices influence the latency, reliability, overhead, and security of dataset synchronization. We also discuss the relationship between transport and application naming, the implications of namespace encoding for Sync group scalability, and the fundamental reason behind the need for Sync Interest multicast.

关键词： Named Data networking distributed Dataset Synchronization Sync Protocols ndN Transport

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GNSGA: A Decentralized Data Replication Algorithm for Big Science Data 22

GNSGA: A Decentralized Data Replication Algorithm for Big Sc...

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22nd international-Federation-for-Information-Processing (IFIP) networking conference (IFIP networking)

作者： Wang, Xi Ai, Xusheng Feltus, F. Alex Shannigrahi, Susmit Tennessee Technol Univ Cookeville TN USA Clemson Univ Clemson SC USA

ISBN: (纸本)9783903176577

Domain science applications in fields such as Genomics and High-Energy Particle Physics use geographically distributed data federations for publishing and accessing datasets. Data is typically replicated among data federation nodes to improve efficiency and fault tolerance. While replication strategies are well documented in distributed database instances (e.g., Apache Cassandra), replication among distributed data storage nodes can be ad-hoc. Replication over wide area networks can also require global coordination (or global shared state) which is not ideal when multiple organizations are involved. In this paper, we introduce GNSGA, which stands for Greedy Non-dominated Sorting Genetic Algorithm II. It is an optimization algorithm that combines greedy and non-dominated sorting genetic algorithms to solve multi-objective optimization problems. The "greedy" aspect of the algorithm refers to the use of a greedy strategy in the selection of nodes, while the "Non-dominated Sorting Genetic Algorithm II (NSGA-II)" is a fast non-dominated multi-objective optimization algorithm with an elite retention strategy. Replication decisions in GNSGA are based on the local properties and resource availability of the data storage nodes. By incorporating Greedy and NSGA-II algorithms, GNSGA optimizes multiple conflicting objectives to satisfy replica placement constraints such as cost, time, and storage capacity. We compared GNSGA with popular replica placement strategies, such as closest node replication, shortest transfer time, and a Particle Swarm Optimization (PSO)-based replication algorithm. We performed simulations and an actual deployment on the NSF's FABRIC testbed for evaluation. The results demonstrate that GNSGA consistently selects nodes to reduce replication time by 5.8%-15.4% while satisfying replication constraints (i.e., cost, time, and storage). We also show that GNSGA is beneficial for replicating large files over wide area networks.

关键词： replication multi-objective optimization distributed federation

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