2022 IEEE Future Networks World Forum (FNWF)最新文献

Comparison of Traditional ML Algorithms for Energy Consumption Prediction Models 能源消耗预测模型的传统ML算法比较

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00048

Rebeca L. Estrada, Víctor Asanza, Danny Torres, Irving Valeriano, Daniel Alvarado

{"title":"Comparison of Traditional ML Algorithms for Energy Consumption Prediction Models","authors":"Rebeca L. Estrada, Víctor Asanza, Danny Torres, Irving Valeriano, Daniel Alvarado","doi":"10.1109/FNWF55208.2022.00048","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00048","url":null,"abstract":"Data centers consume a large amount of energy to meet the increasing demand for IT infrastructure and software due to the IT equipment and cooling infrastructure involved. Therefore, it is necessary to have energy consumption control strategies for DC computer equipment that will allow infrastructure upgrades to reduce energy consumption and to meet the requirement of Green IT. In this way, energy consumption is reduced and the use of technological resources can be optimized. In this paper, we propose to evaluate several traditional Machine Learning algorithms as prediction models using three different temporal windows (i.e. minute, hour and day) taking into account several features such as voltage, energy, frequency, current, power, power factor, and temperature. A comparison of the root square mean error (RMSE) during the validation stage is carried out in order to select the most appropriate algorithm for each time window. In addition, running times are calculated to determine the feasibility of the selected algorithms. Moreover, the suitable predictive model can be the key to the ensure a fair distribution of the workload among the different servers in a Datacenter.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115437977","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

Distributed Channel Access with no Multiple Access Interference in Multi-Hop Wireless Networks 多跳无线网络中无多址干扰的分布式信道接入

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00057

Dylan Cirimelli-Low, J. Garcia-Luna-Aceves

引用次数: 0

Machine Learning Aided Design of Sub-Array MIMO Antennas for CubeSats Based on 3D Printed Metallic Ridge Gap Waveguides 基于3D打印金属脊隙波导的立方体卫星子阵列MIMO天线的机器学习辅助设计

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00080

Mohammed Farouk Nakmouche, D. Deslandes, G. Gagnon

引用次数: 1

5G-MOBIX: The Spain - Portugal Cross - Border Corridor, results of 5G application in shuttle vehicle use cases 5G- mobix:西班牙-葡萄牙跨境走廊，5G在穿梭车用例中的应用结果

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00017

Marta Miranda Dopico, Irene Saco López, Ignacio Benito Frontelo, Jaime Jesús Ruiz Alonso

引用次数: 0

Reviewing the role of machine learning and artificial intelligence for remote attestation in 5G+ networks 回顾机器学习和人工智能在5G+网络中远程认证中的作用

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00111

Shannon K. Gallagher, Austin Whisnant, A. Hristozov, Amit Vasudevan

引用次数: 0

5GRAIL paves the way to the Future Railway Mobile Communication System Introduction grail为未来铁路移动通信系统的引入铺平了道路

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00018

Vassiliki Nikolopoulou, Dan Mandoc, Farid Bazizi, Michaela Klöcker, Sébastien Tardif, Bernd Holfeld, Guillaume Jornod, Nazih Salhab, M. Berbineau, S. Gogos

引用次数: 3

Towards Private 5G O-RAN Implementation: Performance and Business Validation 迈向私有5G O-RAN实现:性能和业务验证

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00124

V. Sathya, Lyutianyang Zhang, M. Yavuz

引用次数: 1

ETSI ZSM Driven Security Management in Future Networks 未来网络中ZSM驱动的安全管理

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00065

Geoffrey Chollon, D. Ayed, Rodrigo Asensio Garriga, Alejandro Molina Zarca, A. Gómez-Skarmeta, M. Christopoulou, Wissem Soussi, Gürkan Gür, U. Herzog

引用次数: 1

Security and 5G: Attack mitigation using Reinforcement Learning in SDN networks 安全性与5G:在SDN网络中使用强化学习缓解攻击

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00114

Jose Alvaro Fernandez-Carrasco, Lander Segurola-Gil, Francesco Zola, Raul Orduna Urrutia

{"title":"Security and 5G: Attack mitigation using Reinforcement Learning in SDN networks","authors":"Jose Alvaro Fernandez-Carrasco, Lander Segurola-Gil, Francesco Zola, Raul Orduna Urrutia","doi":"10.1109/FNWF55208.2022.00114","DOIUrl":"https://doi.org/10.1109/FNWF55208.2022.00114","url":null,"abstract":"5G ecosystem is shaping the future of communication networks enabling innovation and digital transformation not only for individual users but also for companies, industries, and communities. In this scenario, technologies such as Software Defined Networking (SDN) represent a solution for telecommunications providers to create agile, scalable, efficient platforms capable of meeting the requirements in the 5G ecosystem. However, as network environments and systems become increasingly complex, both in terms of size and dynamic behavior, the number of vulnerabilities in them can be very high. In addition, hackers are continuously improving intrusion methods, which are becoming more difficult to detect. For this reason, in this study, we deploy a system based on a Reinforcement Learning (RL) agent capable of applying different countermeasures to defend a network against intrusion and DDoS attacks using SDN. The approach is drawn like a serious game in which a defender and an attacker carry out actions based on the observations they get from the environment, i.e., network current status. In this study, defenders and attackers are trained using the Deep Q-Learning (DQN) algorithm with some variations, like Prioritized Replay, Dueling, and Double DQN, comparing their results in order to get the best strategy for attack mitigation. The results of this paper show that RL algorithms can be successfully used to create more versatile agents able of interpreting and adapting themselves to different situations and so run the best countermeasure to protect the network. According to the results, it is also shown that the Complete strategy, which includes the three DQN variations analyzed, is the one that allows obtaining agents with the best decision making to respond to attacks.","PeriodicalId":300165,"journal":{"name":"2022 IEEE Future Networks World Forum (FNWF)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133742909","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

In-Slice Management Decomposition and Implementation Issues 片内管理分解和实现问题

2022 IEEE Future Networks World Forum (FNWF) Pub Date : 2022-10-01 DOI: 10.1109/FNWF55208.2022.00073

S. Kukliński

引用次数: 1