2023 IEEE 9th International Conference on Network Softwarization (NetSoft)最新文献

Declarative Provisioning of Virtual Network Function Chains in Intent-based Networks 基于意图的网络中虚拟网络功能链的声明式供应

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175449

Jacopo Massa, Stefano Forti, F. Paganelli, Patrizio Dazzi, Antonio Brogi

引用次数: 0

Showcasing In-Switch Machine Learning Inference 展示交换机器学习推理

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175464

Aristide T.-J. Akem, Beyza Bütün, Michele Gucciardo, M. Fiore

引用次数: 0

Energy Optimization of Distributed Video Processing System using Genetic Algorithm with Bayesian Attractor Model 基于贝叶斯吸引子模型的遗传算法的分布式视频处理系统能量优化

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175483

H. Shimonishi, M. Murata, G. Hasegawa, Nattaon Techasarntikul

{"title":"Energy Optimization of Distributed Video Processing System using Genetic Algorithm with Bayesian Attractor Model","authors":"H. Shimonishi, M. Murata, G. Hasegawa, Nattaon Techasarntikul","doi":"10.1109/NetSoft57336.2023.10175483","DOIUrl":"https://doi.org/10.1109/NetSoft57336.2023.10175483","url":null,"abstract":"For the future cyber-physical system (CPS) society, it is necessary to construct digital twins (DTs) of a real world in real time using a lot of cameras and sensors. Hence, the energy efficiency of both networks and computers for largescale distributed video analysis is a major challenge for the full-scale spread of CPSs and DTs. Toward this goal, we first propose a model to arbitrarily split and distribute the video analysis task to terminals, edge servers, and cloud servers and dynamically assign appropriate CNN models to them. System-wide optimization of such distributed processing can reduce overall system power consumption by reducing network bandwidth and efficiently utilizing distributed CPU/GPU resources. To realize this optimization in a real system, we also propose a model to estimate the GPU load, processing time, and power consumption of these devices based on massive experimental measurements. Since such a large-scale optimization is difficult because of the dynamic and multi-objective nature of the problem, we propose a new optimization algorithm composed of Genetic Algorithm and Bayesian Attractor Model. Finally, simulation evaluations are performed to demonstrate that the proposed method can minimize system power consumption and satisfy latency and recognition accuracy requirements of each video analysis, even under changing environmental conditions.","PeriodicalId":223208,"journal":{"name":"2023 IEEE 9th International Conference on Network Softwarization (NetSoft)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131764356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning Edge Workload Orchestrator for Vehicular Edge Computing 用于车辆边缘计算的深度强化学习边缘工作负载协调器

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175484

Eliana Neuza Silva, Fernando Mira da Silva

引用次数: 0

Software Defined Wide Area Networks: Current Challenges and Future Perspectives 软件定义的广域网:当前的挑战和未来的展望

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175458

Annalisa Navarro, R. Canonico, A. Botta

引用次数: 0

Towards Mobility Management in MEC Simulation 浅谈MEC仿真中的移动性管理

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175403

Paulo Araújo, H. López, João Faria, Alexandre J. T. Santos

引用次数: 0

Process Slicing: A New Mitigation Tool for Cyber-attacks against Softwarised Industrial Environments 过程切片:针对软件化工业环境的网络攻击的一种新的缓解工具

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175447

Angel M. Gama Garcia, J. A. Calero, H. Mora, Q. Wang

{"title":"Process Slicing: A New Mitigation Tool for Cyber-attacks against Softwarised Industrial Environments","authors":"Angel M. Gama Garcia, J. A. Calero, H. Mora, Q. Wang","doi":"10.1109/NetSoft57336.2023.10175447","DOIUrl":"https://doi.org/10.1109/NetSoft57336.2023.10175447","url":null,"abstract":"With the evolution of softwarised industrial infrastructures, there is an increasing need for more sophisticated cyber security solutions that can protect industrial processes from a rapidly evolving landscape of cyber threats. In this context, we present an agent-based approach that provides process monitoring, predictive process behaviour, and process control to give the organisations appropriate situational awareness in relation to cyber security threats, enabling them to re-actively or pro-actively detect attacks and respond to advanced persistent threats and multi-vector attacks. Our architectural solution is based on four agents: Process Inventory Agent (PIA), Process Monitoring Agent (PMA), Process Forecasting Agent (PFA), and the Process Slicing Control Agent (PSCA), which work together to deliver a novel mitigation tool to secure softwarised industrial environments. The architecture has been designed, prototyped, and validated in order to demonstrate the effectiveness of our solution. Experimental results show that the proposed solution can successfully mitigate different attacks in the concerned context.","PeriodicalId":223208,"journal":{"name":"2023 IEEE 9th International Conference on Network Softwarization (NetSoft)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128598421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QoEyes: Towards Virtual Reality Streaming QoE Estimation Entirely in the Data Plane QoEyes:完全在数据平面上实现虚拟现实流QoE估计

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175463

F. Vogt, F. R. Cesen, Ariel Góes deCastro, M. C. Luizelli, Christian Esteve Rothenberg, Gergely Pongrácz

引用次数: 2

Data Labeling for Fault Detection in Cloud: A Test Suite-Based Active Learning Approach 云环境中故障检测的数据标注:一种基于测试套件的主动学习方法

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175492

Prateek Bagora, Amin Ebrahimzadeh, F. Wuhib, R. Glitho

{"title":"Data Labeling for Fault Detection in Cloud: A Test Suite-Based Active Learning Approach","authors":"Prateek Bagora, Amin Ebrahimzadeh, F. Wuhib, R. Glitho","doi":"10.1109/NetSoft57336.2023.10175492","DOIUrl":"https://doi.org/10.1109/NetSoft57336.2023.10175492","url":null,"abstract":"Ensuring the quality of service of applications deployed in inherently complex and fault-prone cloud environments is of utmost concern. While machine learning based fault management solutions help attain the desired reliability, they require labeled cloud metrics data for training and evaluation. Furthermore, high dynamicity of cloud environments brings forth emerging data distributions, which necessitate frequent labeling of data for model adaptation. We propose a test suite-based active learning framework for automated labeling of cloud metrics data with the corresponding cloud system state while accounting for emerging fault patterns and data or concept drifts. We have implemented our solution on a cloud testbed and introduced various emerging data distribution scenarios to evaluate the proposed framework’s labeling efficacy over known and emerging data distributions. According to our results, the proposed framework achieves a 41% higher weighted Fl-score and a 34% higher average AUC score than a system without any adaptation for emerging data distributions.","PeriodicalId":223208,"journal":{"name":"2023 IEEE 9th International Conference on Network Softwarization (NetSoft)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130089215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer Learning-Based QoE Estimation For Different Cloud Gaming Contexts 基于迁移学习的不同云游戏环境QoE估计

2023 IEEE 9th International Conference on Network Softwarization (NetSoft) Pub Date : 2023-06-19 DOI: 10.1109/NetSoft57336.2023.10175441

Marcos Carvalho, Daniel Soares, D. Macedo

{"title":"Transfer Learning-Based QoE Estimation For Different Cloud Gaming Contexts","authors":"Marcos Carvalho, Daniel Soares, D. Macedo","doi":"10.1109/NetSoft57336.2023.10175441","DOIUrl":"https://doi.org/10.1109/NetSoft57336.2023.10175441","url":null,"abstract":"Cloud Gaming renders game data in the cloud and forwards it to players over the network. While this reduces hardware costs for players, it introduces challenges in network management and delivering a good gaming experience. In this context, network providers are encouraged to implement QoE-aware management systems to guarantee a desired Quality of Experience (QoE), in which Machine Learning (ML) models achieve the state-of-the-art on QoE estimation/monitoring. However, it is hard to create ML models that generalize to different contexts, especially since QoE perception is subjective and varies among games and players. This paper employs transfer learning and fine-tuning to adjust a source model to different target domains. First, we performed a subjective QoE assessment with real users playing on a realistic testbed. Based on this, we derived four datasets, one being the source dataset (to create the source model) and three distinct target datasets. Experiments show that transfer learning can decrease the average MSE error by at least 41.6% compared to the source model performance on the target datasets while decreasing the demand for labeled data by at least 81.1%. Furthermore, the improvement is greater when compared to models trained from scratch for each target dataset.","PeriodicalId":223208,"journal":{"name":"2023 IEEE 9th International Conference on Network Softwarization (NetSoft)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121609793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0