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Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Mahzabeen Emu, Salimur Choudhury
2021 IEEE Open Journal of the Computer Society  
In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions.  ...  Overall, multi-access edge computing can intensify the performance of delay-sensitive IoT applications compared to the core cloud based VNF deployments.  ...  Finally, at the end of this algorithm as suggested in 36 Algorithm 2: Ensemble testing phase of deep learning aided VNF Deployment at each DC t ∈ M.  ... 
doi:10.1109/ojcs.2021.3098462 fatcat:mycl3xng55hnjkhczkb46axa5u

Internet of Things 2.0: Concepts, Applications, and Future Directions

Ian Zhou, Imran Makhdoom, Negin Shariati, Muhammad Ahmad Raza, Rasool Keshavarz, Justin Lipman, Mehran Abolhasan, Abbas Jamalipour
2021 IEEE Access  
However, machine learning and deep learning based IoT systems are susceptible to "Butterfly Effect."  ...  Thus, this architecture reduces the time of service development, service deployment, and service configuration. iTaaS only reduces complexity and interoperability issues for software deployment.  ... 
doi:10.1109/access.2021.3078549 fatcat:g5jkc5p6tngpfonbhtsbcjipai

2020 Index IEEE Internet of Things Journal Vol. 7

2020 IEEE Internet of Things Journal  
., Rateless-Code-Based Secure Cooperative Transmission Scheme for Industrial IoT; JIoT July 2020 6550-6565 Jamalipour, A., see Murali, S., JIoT Jan. 2020 379-388 James, L.A., see Wanasinghe, T.R.,  ...  Dai, B., +, JIoT Jan. 2020 99-115 Joint DNN Partition Deployment and Resource Allocation for Delay-Sensitive Deep Learning Inference in IoT.  ...  ., +, JIoT March 2020 1582-1593 An Ensemble of Deep Recurrent Neural Networks for Detecting IoT Cyber Attacks Using Network Traffic.  ... 
doi:10.1109/jiot.2020.3046055 fatcat:wpyblbhkrbcyxpnajhiz5pj74a

Table of Contents

2019 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)  
Cloud Deployments using Ansible 336-339 65 615 Object Detection and Recognition using Faster RCNN and Deep Learning 340-344 66 197 Comparative Analysis of Induction Motor Fault Detection  ...  STUDSAT-2 90-94 19 695 Automated Regression Testing and Data Analytics using Python 95-99 20 687 Efficient Breast Cancer Prediction Using Ensemble Machine 100-104 Learning Models 21  ... 
doi:10.1109/rteict46194.2019.9016925 fatcat:3azpzwfoljdhpkx5h3addkc4ga

D2.3 Network Architecture Definition, Design Methods and Performance Evaluation

António Eira, João Pedro, Marc Ruiz, Luis Velasco, Jaume Comellas, Gabriel Junyent, Ramon Casellas, Raül Muñoz, Laia Nadal, Michela Svaluto Moreolo, Marco Quagliotti, Anna Chiadò Piat (+25 others)
2019 Zenodo  
The overall Metro-Haul objective is to architect and design cost-effective, energy-efficient, agile and programmable metro networks that are scalable for 5G access and future requirements, encompassing  ...  /security for ubiquitous new service deployments.  ...  native libraries to aid in the development of optimization for joint IT/optical network optimization.  ... 
doi:10.5281/zenodo.3496938 fatcat:3duf4xxqdfg5xgri57krrlmz5u

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Ijaz Ahmad, Shariar Shahabuddin, Hassan Malik, Erkki Harjula, Teemu Leppanen, Lauri Loven, Antti Anttonen, Ali Hassan Sodhro, Muhammad Mahtab Alam, Markku Juntti, Antti Yla-Jaaski, Thilo Sauter (+3 others)
2020 IEEE Access  
Future research directions are drawn to help the research community to circumvent the challenges of future services (e.g. for massive IoT) and technologies (e.g.  ...  Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services.  ...  In [95] , a practical deep learning aided polar decoder is presented for any code length. The computational complexity of the proposed decoder is close to the original belief propagation algorithm.  ... 
doi:10.1109/access.2020.3041765 fatcat:erbcetvcrjabrl4qloow3dqcai

Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems [article]

Shashikant Ilager, Rajeev Muralidhar, Rajkumar Buyya
2020 arXiv   pre-print
Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task.  ...  On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response.  ...  For example, the cognitive neural nets, which are basic building blocks for many regressions, classification, and Deep Learning (DL) algorithms primarily rely on the principles of stochasticity for different  ... 
arXiv:2006.05075v2 fatcat:tck54mz4yff3deesetflj34wke

AI and ML – Enablers for Beyond 5G Networks

Alexandros Kaloxylos, Anastasius Gavras, Daniel Camps Mur, Mir Ghoraishi, Halid Hrasnica
2020 Zenodo  
Hybrid solutions are presented such as combined analytical and machine learning modelling as well as expert knowledge aided machine learning.  ...  Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data.  ...  These methods are very powerful tools, suitable for complex environment settings like resource allocation and orchestration, VNF deployments, etc.  ... 
doi:10.5281/zenodo.4299895 fatcat:ngzbopfm6bb43lnrmep6nz5icm


2020 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)  
Recently, deep learning models have been used for visual saliency prediction.  ...  Our primary purpose is to find the optimal VNF placement reducing resource consumption while providing specific latency and throughput for slicing services.  ...  for a variety of energy services.  ... 
doi:10.1109/ccece47787.2020.9255763 fatcat:mpf7smikpfc77bu73ciqstdagm

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
In particular, deep learning based solutions can automatically extract features from raw data, without human expertise.  ...  This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering  ...  Coninck et al. consider distributing deep learning over IoT for classification ap- plications [170].  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Yanling Zhao, Ye Li, Xinchang Zhang, Guanggang Geng, Wei Zhang, Yanjie Sun
2019 IEEE Access  
INDEX TERMS Artificial intelligence, machine learning, network management, software-defined networking.  ...  This paper is necessary and helpful for researchers from different fields to accurately master the key issues.  ...  deployment.  ... 
doi:10.1109/access.2019.2928564 fatcat:r4ds5ot6u5a3tl4wzdvahwb7xa

The Roadmap to 6G Security and Privacy

Pawani Porambage, Gurkan Gur, Diana Pamela Moya Osorio, Madhusanka Liyanage, Andrei Gurtov, Mika Ylianttila
2021 IEEE Open Journal of the Communications Society  
All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.  ...  Different ML types (e.g., neural network, deep learning, supervised learning) can be applied for privacy protection in terms of data, image, location, and communication (e.g.  ...  These services may include, however not limited to, secure VNF management, secure slice brokering, automated Security SLA management, scalable IoT PKI management, secure roaming and offloading handling  ... 
doi:10.1109/ojcoms.2021.3078081 fatcat:r5g662rcxjcgvjfc2el3lesdzy

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, Sara Ayoubi, Nashid Shahriar, Felipe Estrada-Solano, Oscar M. Caicedo
2018 Journal of Internet Services and Applications  
There are various surveys on ML for specific areas in networking or for specific network technologies.  ...  Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains.  ...  Acknowledgments We thank the anonymous reviewers for their insightful comments and suggestions that helped us improve the quality of the paper.  ... 
doi:10.1186/s13174-018-0087-2 fatcat:jvwpewceevev3n4keoswqlcacu

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Hazim Shakhatreh, Ahmad H. Sawalmeh, Ala Al-Fuqaha, Zuochao Dou, Eyad Almaita, Issa Khalil, Noor Shamsiah Othman, Abdallah Khreishah, Mohsen Guizani
2019 IEEE Access  
We also discuss current research trends and provide future insights for potential UAV uses.  ...  Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges.  ...  For real time application, deep learning requires the usage of on-board GPUs.  ... 
doi:10.1109/access.2019.2909530 fatcat:xgknpyuqazhpvferjkkdohxmtu

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

Prateek Shantharama, Akhilesh S. Thyagaturu, Martin Reisslein
2020 IEEE Access  
g: DEEP LEARNING (DL) BOOST The Deep Learning (DL) Boost IS acceleration on Intel R CPUs [32] targets machine learning and neural network computations.  ...  In contrast to NFV management, the orchestration of service function chaining (SFC) adds more complexity since an SFC involves the management of multiple VNFs for a single network service.  ... 
doi:10.1109/access.2020.3008250 fatcat:kv4znpypqbatfk2m3lpzvzb2nu
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