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Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

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arXiv 2022年

作者： Kokkonen, Henna Lovén, Lauri Motlagh, Naser Hossein Kumar, Abhishek Partala, Juha Nguyen, Tri Pujol, Víctor Casamayor Kostakos, Panos Leppänen, Teemu González-Gil, Alfonso Sola, Ester Angulo, Iñigo Liyanage, Madhusanka Bennis, Mehdi Tarkoma, Sasu Dustdar, Schahram Pirttikangas, Susanna Riekki, Jukka Center for Ubiquitous Computing University of Oulu Finland Center for Machine Vision and Signal Analysis University of Oulu Finland Center for Wireless Communications University of Oulu Finland School of Computer Science University College Dublin Ireland Department of Computer Science University of Helsinki Finland Distributed Systems Group TU Wien Austria R&D Department *** S.L. Spain

Future AI applications require performance, reliability, and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration. © 2022, CC BY-NC-ND.

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

18th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

Analysis of the Resilience of an Emergency department: The Case of Accident with Multiple Victims 23

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Analysis of the Resilience of an Emergency Department: The C...

Proceedings of the Winter Simulation Conference

作者： Mariela Rodriguez Francesc Boixader Eva Bruballa Francisco Epelde Armando De Giusti Alvaro Wong Dolores Rexach Emilio Luque Faculty of Engineering National University of Jujuy Jujuy ARGENTINA Escoles Universitaries Gimbernat (EUG) Computer Science School Universitat Autonoma de Barcelona Sant Cugat del Vallès Barcelona SPAIN Consultant Internal Medicine University Hospital Parc Tauli Universitat Autonoma de Barcelona Sadabell Barcelona SPAIN Faculty of Informatics National University of La Plata Buenos Aires ARGENTINA Computer Architecture and Operating Systems Department Universitat Autonoma de Barcelona Bellaterra Barcelona SPAIN

ISBN: (纸本)9798350369663

The care of multiple victims such as in natural disasters in an Emergency department is critical. This differs from ordinary care by the number of patients that arrive, their severity and insufficient staff for these events. Designing and simulating this real life scenario will be useful for disaster management decision makers. The objective of this simulation is to model a system with resilience to critical situations. To model the input of this research, we worked with the percentage of patients received by Cauquenes Hospital during the Chilean Earthquake on February 27th, 2010. A comparison of two situations has been carried out: the admission of patients before an earthquake with normal daily attention versus the admission of patients before an earthquake and the activation of the relief chain. The latter situation allows the system to be resilient and adapt quickly to its new reality.

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Intrusion Detection systems using Data Mining Techniques: A comparative study 11

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Intrusion Detection Systems using Data Mining Techniques: A ...

11th International Conference on Information systems and Advanced Technologies, ICISAT 2021

作者： Haddadi, Mohamed Khiat, Abdelhamid Bahnes, Nacera Université m'Hamed Bougara de Boumerdes Faculté des Sciences Économiques Commerciales et des Sciences de Gestion Département des Sciences Commerciales Avenue de l'indépendence Boumerdes35000 Algeria Research Center on Scientific and Technical Information Networks and Distributed Systems Division Algeria Computer Science Department University of Oran 1 Ahmed Ben Bella PO Box 1524 El M'naouar Oran Algeria Computer Science Department Faculty of Exact Sciences and Computer Science University of Abdelhamid Ibn Badis Mostaganem27000 Algeria

ISBN: (纸本)9781665478243

Data mining tools are widely used in computer networks. The well-known and mostly used tools to secure computers and network systems are WEKA and TANAGRA. The purpose of this study is to compare these two tools in terms of detection accuracy and computation time. This comparison was conducted using a well-known NSL-KDD dataset. Experiments show that TANAGRA achieves better results than WEKA in detection accuracy. But, TANAGRA is competitive with WEKA in terms of computation time. © 2021 IEEE.

关键词： Data mining

Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction

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Comparative Analysis of Machine Learning Models for Diabetes...

2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Ismail, Leila Materwala, Huned United Arab Emirates University College of Information Technology Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu-Dhabi Al-Ain15551 United Arab Emirates

ISBN: (纸本)9781728176246

Diabetes is one of the top 10 causes of death worldwide. Health professionals are aiming for machine learning models to support the prognosis of diabetes for better healthcare and to put in place an effective prevention plan. In this paper, we conduct a comparative analysis of the most used machine learning models in the literature to predict the prevalence of diabetes mellitus type 2. We evaluate the models in terms of accuracy, F-measure and execution time with and without feature selection using a real-life diabetes dataset. The detailed analysis is in the paper. © 2020 IEEE.

关键词： Medical informatics

Exploiting open data for CO 2 estimation via artificial intelligence and eXplainable AI

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Expert systems with Applications 2025年 291卷

作者： Stefano Bilotta Luciano Alessandro Ipsaro Palesi Paolo Nesi University of Florence Department of Mathematics and Computer Science “U. Dini” Firenze 50134 Italy University of Florence Distributed Systems and Internet Technology DISIT Lab Firenze 50139 Italy

Climate change is a challenge of global relevance that requires in-depth understanding and immediate response. Urban CO 2 emissions are one of the main causes of climate change, and their estimation is crucial for planning actions aimed at creating more sustainable cities. Currently, models for estimating CO 2 emissions mainly focus on traffic patterns, energy consumption, or a limited set of socio-economic factors, often overlooking the increasing role of urban services in today’s cities. Moreover, these data are rarely accessible, which limits their usefulness for policy design. In the present paper, a general CO 2 emission estimation approach, based on a large range of often accessible open data as predictors, is presented. Such open data are related to human activity regarding services and (socio-economic) conditions arising in a given (urban) area. The proposed model focuses on fine-scale prediction to better understand the dynamics of emissions via machine learning approaches, while taking into account an innovative study based on open data sources including city services. The resulting best models have been based on XgBoost and GCN (graph convolutional network). The outcomes provided better precision (MAPE in the order of 8%) with respect to the state-of-the-art solutions. The goal has been to understand how specific predictors can contribute to or mitigate CO 2 emissions in the observed area. To this end, the impact of several features has been analyzed in order to identify the related key factors influencing emissions. A formal study has been conducted to perform feature relevance analysis by using eXplainable AI (XAI) approach. The proposed model is useful to define targeted policies reducing the pollutant impact of cities, promote a more ecologically sustainable urban lifestyle and improve sustainable urban planning. Both solutions and models have been assessed and improved, so as to be more flexible by using some transfer learning techniques. This resear

关键词： CO 2 emissions City services Machine learning Deep learning eXplainableAI

Towards Holographic Video Communications: A Promising AI-driven Solution

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arXiv 2022年

作者： Huang, Yakun Zhu, Yuanwei Qiao, Xiuquan Su, Xiang Dustdar, Schahram Zhang, Ping The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science Norwegian University of Science and Technology Gjøvik2815 Norway University of Oulu Oulu90570 Finland The Distributed Systems Group Technische Universität Wien Vienna1040 Austria

Real-time holographic video communications enable immersive experiences for next-generation video services in the future metaverse era. However, high-fidelity holographic videos require high bandwidth and significant computation resources, which exceed the transferring and computing capacity of 5G networks. This article reviews state-of-the-art holographic point cloud video (PCV) transmission techniques and highlights the critical challenges of delivering such immersive services. We further implement a preliminary prototype of an AI-driven holographic video communication system and present critical experimental results to evaluate its performance. Finally, we identify future research directions and discuss potential solutions for providing real-time and high-quality holographic experiences. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Multi-Player Immersive Communications and Interactions in Metaverse: Challenges, Architecture, and Future Directions

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arXiv 2022年

作者： Huang, Yakun Qiao, Xiuquan Wang, Haowen Su, Xiang Dustdar, Schahram Zhang, Ping State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science Norwegian University of Science and Technology 2815 Gjøvik Norway University of Oulu Oulu90570 Finland Distributed Systems Group Technische Universität Wien Vienna1040 Austria

The metaverse has awakened users’ expectations of an immersive interaction that fuses the virtual digital world and the physical world across space and time. However, the metaverse is still in its infancy, typically expanding multi-player applications (e.g., multi-player games) to implement a prototype with the help of 5G/Beyond 5G, Artificial Intelligence, digital twin, and other enabling technologies. This article reviews the characteristics, key enabling technologies, and driving applications of the state-of-the-art metaverse. We focus on the immersive interactions perspective of the metaverse from the tasks, inputs, and feedback across the users, digital world, and physical world and reveal the key challenges. Afterwards, we present a multi-player interaction prototype platform based on a cloud-edge-device collaborative framework. Also, we evaluate it with centralized and device-to-device (D2D) approaches to verify the efficiency and flexibility of interactions. Finally, we point out future research approaches and discuss potential solutions to enable more stable and higher quality multi-player interactions for metaverse services. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

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arXiv 2021年

作者： Díaz Ferreyra, Nicolás E. Hecking, Tobias Aïmeur, Esma Heisel, Maritta Hoppe, H. Ulrich University of Duisburg-Essen Department of Computer Science and Applied Cognitive Science Duisburg47057 Germany Institute for Software Technology Intelligent and Distributed Systems German Aerospace Center Köln51147 Germany Department of Computer Science and Operations Research University of Montréal Canada

Access-Control Lists (ACLs) (a.k.a. "friend lists") are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as "untrusted") to the emerging communities inside the user’s ego-network. However, unlike users’ access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination. Copyright © 2021, The Authors. All rights reserved.

关键词： Access control