The Road Ahead for Networking: A Survey on ICN-IP Coexistence Solutions

In recent years, the current Internet has experienced an unexpected paradigm shift in the usage model, which has pushed researchers towards the design of the Information-Centric Networking (ICN) paradigm as a possible replacement of the existing architecture. Even though both Academia and Industry have investigated the feasibility and effectiveness of ICN, achieving the complete replacement of the Internet Protocol (IP) is a challenging task. Some research groups have already addressed the coexistence by designing their own architectures, but none of those is the final solution to move towards the future Internet considering the unaltered state of the networking. To design such architecture, the research community needs now a comprehensive overview of the existing solutions that have so far addressed the coexistence. The purpose of this paper is to reach this goal by providing the first comprehensive survey and classification of the coexistence architectures according to their features (i.e., deployment approach, deployment scenarios, addressed coexistence requirements and architecture or technology used) and evaluation parameters (i.e., challenges emerging during the deployment and the runtime behaviour of an architecture). We believe that this paper will finally fill the gap required for moving towards the design of the final coexistence architecture.Comment: 23 pages, 16 figures, 3 table

IoT data processing pipeline in FoF perspective

With the development in the contemporary industry, the concepts of ICT and IoT are gaining more importance, as they are the foundation for the systems of the future. Most of the current solutions converge into transforming the traditional industry in new smart interconnected factories, aware of its context, adaptable to different environments and capable of fully using its resources. However, the full potential for ICT manufacturing has not been achieved, since there is not a universal or standard architecture or model that can be applied to all the existing systems, to tackle the heterogeneity of the existing devices. In a common factory, exists a large amount of information that needs to be processed into the system in order to define event rules accordingly to the related contextual knowledge, to later execute the needed actions. However, this information is sometimes heterogeneous, meaning that it cannot be accessed or understood by the components of the system. This dissertation analyses the existing theories and models that may lead to seamless and homogeneous data exchange and contextual interpretation. A framework based on these theories is proposed in this dissertation, that aims to explore the situational context formalization in order to adequately provide appropriate actions

Exploring the use of data compression for accelerating machine learning in the edge with remote virtual graphics processing units

[EN] Internet of Things (IoT) devices are usually low performance nodes connected by low bandwidth networks. To improve performance in such scenarios, some computations could be done at the edge of the network. However, edge devices may not have enough computing power to accelerate applications such as the popular machine learning ones. Using remote virtual graphics processing units (GPUs) can address this concern by accelerating applications leveraging a GPU installed in a remote device. However, this requires exchanging data with the remote GPU across the slow network. To address the problem with the slow network, the data to be exchanged with the remote GPU could be compressed. In this article, we explore the suitability of using data compression in the context of remote GPU virtualization frameworks in edge scenarios executing machine learning applications. We use popular machine learning applications to carry out such exploration. After characterizing the GPU data transfers of these applications, we analyze the usage of existing compression libraries for compressing those data transfers to/from the remote GPU. Our exploration shows that transferring compressed data becomes more beneficial as networks get slower, reducing transfer time by up to 10 times. Our analysis also reveals that efficient integration of compression into remote GPU virtualization frameworks is strongly required.European Union's Horizon 2020 Research and Innovation Programme, Grant/Award Numbers: 101016577, 101017861.Peñaranda-Cebrián, C.; Reaño, C.; Silla, F. (2022). Exploring the use of data compression for accelerating machine learning in the edge with remote virtual graphics processing units. Concurrency and Computation: Practice and Experience. 35(20):1-19. https://doi.org/10.1002/cpe.7328119352

Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises can not afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives

FPGA based technical solutions for high throughput data processing and encryption for 5G communication: A review

The field programmable gate array (FPGA) devices are ideal solutions for high-speed processing applications, given their flexibility, parallel processing capability, and power efficiency. In this review paper, at first, an overview of the key applications of FPGA-based platforms in 5G networks/systems is presented, exploiting the improved performances offered by such devices. FPGA-based implementations of cloud radio access network (C-RAN) accelerators, network function virtualization (NFV)-based network slicers, cognitive radio systems, and multiple input multiple output (MIMO) channel characterizers are the main considered applications that can benefit from the high processing rate, power efficiency and flexibility of FPGAs. Furthermore, the implementations of encryption/decryption algorithms by employing the Xilinx Zynq Ultrascale+MPSoC ZCU102 FPGA platform are discussed, and then we introduce our high-speed and lightweight implementation of the well-known AES-128 algorithm, developed on the same FPGA platform, and comparing it with similar solutions already published in the literature. The comparison results indicate that our AES-128 implementation enables efficient hardware usage for a given data-rate (up to 28.16 Gbit/s), resulting in higher efficiency (8.64 Mbps/slice) than other considered solutions. Finally, the applications of the ZCU102 platform for high-speed processing are explored, such as image and signal processing, visual recognition, and hardware resource management

AI Techniques for COVID-19

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses

AI Techniques for COVID-19

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses