2022 18th International Conference on Network and Service Management (CNSM)最新文献_第3页

Monitoring Metrics for Load Balancing over Video Content Distribution Servers 视频内容分发服务器上的负载平衡监控指标

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964896

Edenilson Jônatas dos Passos, Adriano Fiorese

{"title":"Monitoring Metrics for Load Balancing over Video Content Distribution Servers","authors":"Edenilson Jônatas dos Passos, Adriano Fiorese","doi":"10.23919/CNSM55787.2022.9964896","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964896","url":null,"abstract":"Cloud computing and video streaming services have been in constant expansion in recent years. Along with it, the demand for computing resources has also increased significantly. In this context, monitoring the use of these resources is crucial to maintain a satisfactory level of Quality of Service and, consequently, Quality of Experience, especially in video transmission services. This work discusses a new method of monitoring resources and quality of service metrics on content servers involving CPU utilization and server throughput, which is obtained in a distributed way. For that, a distributed collector system that is based on a modified version of the ring election algorithm is developed to retrieve the Quality of Service metrics in each server. Evaluation experiment results show that there are no performance gains on the system such as the content loading faster for the user, there are however, improvements in terms of the whole system scalability. The greater the number of servers for monitoring, the better the approach is compared to the traditional method of monitoring resources through request and response.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133652099","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

AI-Based mechanism for the Predictive Resource Allocation of V2X related Network Services 基于人工智能的V2X相关网络业务资源预测分配机制

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964722

Asterios Mpatziakas, Anastasios Sinanis, Iosif Hamlatzis, A. Drosou, D. Tzovaras

{"title":"AI-Based mechanism for the Predictive Resource Allocation of V2X related Network Services","authors":"Asterios Mpatziakas, Anastasios Sinanis, Iosif Hamlatzis, A. Drosou, D. Tzovaras","doi":"10.23919/CNSM55787.2022.9964722","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964722","url":null,"abstract":"5G architectures will utilize the virtualization of the network functions (VNF) and the use of Multi-access edge computing (MEC) to gain multiple benefits such as simpler service orchestration, while simultaneously covering diverse use cases even with strict performance requirements. 5G service orchestration mechanisms will need to allow more efficient and flexible network deployment and operations in a resource-efficient and delay-sensitive manner. A field that is expected to be greatly boosted by these advances, is Cellular Vehicle to Everything communications. 5G will enable cooperative, connected and automated mobility services, which are often are safety critical while also having stringent delay requirements. This paper, proposes a mechanism that predicts the future position of a vehicle moving in both urban and/or highway environments. Based on this knowledge, it decides on the optimal position of VNFs so that the allocation of network resources can be preemptively requested. The objective of this mechanism is to ensure the uninterrupted, continuous connections of the vehicles, resulting in minimal or no service interruption time while ensuring an optimal utilization of Edge Cloud and MEC resources.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123497536","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

Maximizing Information Usefulness in Vehicular CP Networks Using Actor-Critic Reinforcement Learning 使用Actor-Critic强化学习最大化车辆CP网络中的信息有用性

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964740

Imed Ghnaya, T. Ahmed, M. Mosbah, H. Aniss

引用次数: 1

Neural Collaborative Filtering for Network Delay Matrix Completion 网络延迟矩阵补全的神经协同滤波

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964607

Sanaa Ghandi, Alexandre Reiffers-Masson, Sandrine Vaton, T. Chonavel

引用次数: 0

Tunneling through DNS over TLS providers 通过TLS提供商的DNS进行隧道

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964617

Lukáš Melcher, Karel Hynek, T. Čejka

引用次数: 0

HSFL: An Efficient Split Federated Learning Framework via Hierarchical Organization HSFL:基于层级组织的高效分离联邦学习框架

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964646

Tengxi Xia, Yongheng Deng, Sheng Yue, Junyi He, Ju Ren, Yaoxue Zhang

{"title":"HSFL: An Efficient Split Federated Learning Framework via Hierarchical Organization","authors":"Tengxi Xia, Yongheng Deng, Sheng Yue, Junyi He, Ju Ren, Yaoxue Zhang","doi":"10.23919/CNSM55787.2022.9964646","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964646","url":null,"abstract":"Federated learning (FL) has emerged as a popular paradigm for distributed machine learning among vast clients. Unfortunately, resource-constrained clients often fail to participate in FL because they cannot pay for the memory resources required for model training due to their limited memory or bandwidth. Split federated learning (SFL) is a novel FL framework in which clients commit intermediate results of model training to a cloud server for client-server collaborative training of models, making resource-constrained clients also eligible for FL. However, existing SFL frameworks mostly require frequent communication with the cloud server to exchange intermediate results and model parameters, which results in significant communication overhead and elongated training time. In particular, this can be exacerbated by the imbalanced data distributions of clients. To tackle this issue, we propose HSFL, a hierarchical split federated learning framework that efficiently trains SFL model through hierarchical organization participants. Under the HSFL framework, we formulate a Cloud Aggregation Time Minimization (CATM) problem to minimize the global training time and design a light-weight client assignment algorithm based on dynamic programming to solve it. Moreover, we develop a self-adaption approach to cope with the dynamic computational resources of clients. Finally, we implement and evaluate HSFL on various real-world training tasks, elaborating on its effectiveness and superiority in terms of efficiency and accuracy compared to baselines.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115826270","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: 4

Open-Source Service Management for a Fully Disaggregated Optical Network Simulation 全分解光网络仿真的开源服务管理

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964941

S. Patri, Shabnam Sultana, Michael Dürre, Saquib Amjad, Aijana Schumann, A. Autenrieth, J. Elbers, T. Bauschert, C. M. Machuca

引用次数: 0

Preventing Control Plane Overload in SDN Networks with Programmable Data Planes 数据平面可编程的SDN网络控制平面过载预防

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964491

Cristian Bermudez Serna, C. M. Machuca

{"title":"Preventing Control Plane Overload in SDN Networks with Programmable Data Planes","authors":"Cristian Bermudez Serna, C. M. Machuca","doi":"10.23919/CNSM55787.2022.9964491","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964491","url":null,"abstract":"Software-Defined Networking (SDN) has redefined the architectural blueprint for designing networks suitable for future applications. Today, the idea of a centralized control plane managing its underlying resources is common for architectures in mobile and industrial networks. Guaranteeing resources availability for optimal operation of the control plane is of vital importance in SDN, since compromising the controller may result in an unforeseen behaviour in the data plane. This work focuses on the SDN reactive configuration mechanism, that although originally designed for the efficient handling of changing conditions in the data plane, it can be easily misused to overload the control plane. Aiming at addressing this problem, the PDP (Programmable Data Plane)-based Controller Protection Protocol (PCPP) is presented. This protocol introduces a mechanism that efficiently filters spoofed requests at the network edge. In PCPP, end-stations require to solve a challenge before sending any connection request to the controller. The challenge answer is checked at the edge switches, which only forward valid requests to the controller. PCPP is implemented using P4, a language for programming PDP-capable devices, and its evaluation is carried out using BMv2 software switches. The results demonstrate the effectiveness of PCPP at protecting bandwidth and processing resources in the control plane against spoofed requests. A comparison against an state-of-the-art alternative not only highlights the higher efficiency of PCPP, but also its application flexibility.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126597385","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: 2

On the Performance of TCP in Reconfigurable Data Center Networks TCP在可重构数据中心网络中的性能研究

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9964863

Kaan Aykurt, Johannes Zerwas, Andreas Blenk, W. Kellerer

{"title":"On the Performance of TCP in Reconfigurable Data Center Networks","authors":"Kaan Aykurt, Johannes Zerwas, Andreas Blenk, W. Kellerer","doi":"10.23919/CNSM55787.2022.9964863","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964863","url":null,"abstract":"Today’s data centers are hosting various applications under the same roof. The diversity among deployed applications leads to a complex traffic mix in Data Center Networks (DCNs). Reconfigurable Data Center Networks (RD-CNs) have been designed to fulfill the demanding requirements of ever-changing data center traffic. However, they pose new challenges for network traffic engineering, e.g., interference between reconfigurations and congestion control (CC). This raises a fundamental research problem: can the current transport layer protocols handle frequent network updates?; This paper focuses on the Transmission Control Protocol (TCP) and presents a measurement study of TCP variants in RDCNs. The quantitative analysis of the measurements shows that migrated flows suffer from frequent reconfigurations. The effect of reconfigurations on the cost, e.g. increased Flow Completion Time (FCT), depending on the traffic mix is modeled with Machine Learning (ML) methods. The availability of such a model will provide insights into the relationship between the reconfiguration settings and the FCT. Our model explains 88% of the variance in the FCT increase under different reconfiguration settings.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793329","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: 1

A Sample Efficient Multi-Agent Approach to Continuous Reinforcement Learning 持续强化学习的样本高效多智能体方法

2022 18th International Conference on Network and Service Management (CNSM) Pub Date : 2022-10-31 DOI: 10.23919/CNSM55787.2022.9965060

Diarmuid Corcoran, P. Kreuger, Magnus Boman

{"title":"A Sample Efficient Multi-Agent Approach to Continuous Reinforcement Learning","authors":"Diarmuid Corcoran, P. Kreuger, Magnus Boman","doi":"10.23919/CNSM55787.2022.9965060","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9965060","url":null,"abstract":"As design, deployment and operation complexity increase in mobile systems, adaptive self-learning techniques have become essential enablers in mitigation and control of the complexity problem. Artificial intelligence and, in particular, reinforcement learning has shown great potential in learning complex tasks through observations. The majority of ongoing reinforcement learning research activities focus on single-agent problem settings with an assumption of accessibility to a globally observable state and action space. In many real-world settings, such as LTE or 5G, decision making is distributed and there is often only local accessibility to the state space. In such settings, multi-agent learning may be preferable, with the added challenge of ensuring that all agents collaboratively work towards achieving a common goal. We present a novel cooperative and distributed actor-critic multi-agent reinforcement learning algorithm. We claim the approach is sample efficient, both in terms of selecting observation samples and in terms of assignment of credit between subsets of collaborating agents.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125780670","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