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The network function virtualization (NFV) paradigm is an emerging technology that provides network flexibility by allowing the allocation of network functions over commodity hardware, like legacy servers in an IT infrastructure. In comparison with traditional network functions, implemented by dedicated hardware, the use of NFV reduces the operating and capital expenses and improves service deployment. In some scenarios, a complete network service can be composed of several functions, following a specific order, known as a service function chain (SFC). SFC placement is a complex task, already proved to be NP-hard. Moreover, in highly distributed scenarios, the network performance can also be impacted by other factors, such as traffic oscillations and high delays. Therefore, a given SFC placement strategy must be carefully developed to meet the network operator service constraints. In this paper, we present a systematic review of SFC placement advances in distributed scenarios. Differently from the current literature, we examine works over the last 10 years which addressed this problem while focusing on distributed scenarios. We then discuss the main scenarios where SFC placement has been deployed, as well as the several techniques used to create the placement strategies. We also present the main goals considered to create SFC placement strategies and highlight the metrics used to evaluate them. [ABSTRACT FROM AUTHOR]

Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil's National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO. [ABSTRACT FROM AUTHOR]

With the popularity of mobile devices, the next generation of mobile networks has faced several challenges. Different applications have been emerged, with different requirements. Offering an infrastructure that meets different types of applications with specific requirements is one of these issues. In addition, due to user mobility, the traffic generated by the mobile devices in a specific location is not constant, making it difficult to reach the optimal resource allocation. In this context, network function virtualization (NFV) can be used to deploy the telecommunication stacks as virtual functions running on commodity hardware to meet users' requirements such as performance and availability. However, the deployment of virtual functions can be a complex task. To select the best placement strategy that reduces the resource usage, at the same time keeps the performance and availability of network functions is a complex task, already proven to be an NP-hard problem. Therefore, in this paper, we formulate the NFV placement as a multi-objective problem, where the risk associated with the placement and energy consumption are taken into consideration. We propose the usage of two optimization algorithms, NSGA-II and GDE3, to solve this problem. These algorithms were taken into consideration because both work with multi-objective problems and present good performance. We consider a triathlon circuit scenario based on real data from the Ironman route as an use case to evaluate and compare the algorithms. The results show that GDE3 is able to attend both objectives (minimize failure and minimize energy consumption), while the NSGA-II prioritizes energy consumption. [ABSTRACT FROM AUTHOR]

With the emergence of new applications driven by the popularization of mobile devices, the next generation of mobile networks faces challenges to meet different requirements. Virtual Network Functions (VNFs) have been deployed to minimize operational costs and make network management more flexible. In this sense, strategies for VNF placement can impact different metrics of interest. Invoking and visiting VNFs in a specific execution order may be required for different use cases, resulting in a complete network service called Service Function Chain (SFC). The SFC placement problem is to define a feasible path in the physical infrastructure whose nodes and edges meet the computational and bandwidth requirements for the VNFs and virtual links, respectively. It has already been proved that this process is NP-hard and it is difficult to find an optimal solution to this problem. Therefore, in this paper, we propose the use of meta-heuristics to solve the SFC placement problem in cellular networks. We consider a triathlon competition leading to different mobility patterns. We collected real data about the competitors to simulate their movements through the scenario as well as the measured signal quality of the network. We formulate the SFC placement problem as a multi-objective problem where we try to minimize the placement cost and the total SFC delay. To solve the problem, we propose the use of two algorithms, NSGA-II and GDE3, which compare two different greedy approaches that prioritize the different optimization metrics considered in this work. Our results show that the meta-heuristics provide better results for each of the metrics. For all competition stages, GDE3 presented a slightly lower placement costs than NSGA-II, while NSGA-II had a lower delay in some scenarios. [ABSTRACT FROM AUTHOR]

This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration. [ABSTRACT FROM AUTHOR]

The term sportswashing has been discussed and analysed within academic circles, as well as the mainstream media. However, the majority of existing research has focused on one-off event-based sportswashing strategies (such as autocratic states hosting major international sports events) rather than longer term investment-based strategies (such as state actors purchasing sports clubs and teams). Furthermore, little has been written about the impact of this latter strategy on the existing fanbase of the purchased team and on their relationship with sportswashing and the discourses surrounding it. This paper addresses this lacuna through analysis of a popular Manchester City online fan forum, which illustrates the manner in which this community of dedicated City fans have legitimated the actions of the club's ownership regime, the Abu Dhabi United Group – a private equity group operated by Abu Dhabi royalty and UAE politicians. The discursive strategies of the City fans are discussed, in addition to the wider significance of these strategies on the issue of sportswashing and its coverage by the media. [ABSTRACT FROM AUTHOR]

Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain. [ABSTRACT FROM AUTHOR]

Cloud data center providers benefit from software-defined infrastructure once it promotes flexibility, automation, and scalability. The new paradigm of software-defined infrastructure helps facing current management challenges of a large-scale infrastructure, and guarantying service level agreements with established availability levels. Assessing the availability of a data center remains a complex task as it requires gathering information of a complex infrastructure and generating accurate models to estimate its availability. This paper covers this gap by proposing a methodology to automatically acquire data center hardware configuration to assess, through models, its availability. The proposed methodology leverages the emerging standardized Redfish API and relevant modeling frameworks. Through such approach, we analyzed the availability benefits of migrating from a conventional data center infrastructure (named Performance Optimization Data center (POD) with redundant servers) to a next-generation virtual Performance Optimized Data center (named virtual POD (vPOD) composed of a pool of disaggregated hardware resources). Results show that vPOD improves availability compared to conventional data center configurations. [ABSTRACT FROM AUTHOR]