Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.
Abstract:Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.Keywords: edge computing; fog computing; CNN; formal modeling; ACP; Promela; Spin
Distributed Computing Principles And Applications M L Liu Pearson Education.rar
Cloud computing is a service model where computing services that are available remotely permit users to access applications and data and physical computation resources over a network, on demand or pay-per-use fashion [135, 136]. The application domains of cloud computing technology in education include e-learning (such as curriculum content management, virtual lab environment, office productivity suite, library management, and collaborative learning), communication (e-mail and notifications), and administration (such as students registration management and human resources management) [137]. Cloud computing has not only been used in the education sector but also in other sectors such as healthcare [138], manufacturing, entertainment, transportation, and energy [139].
Over the years, cloud computing has been used for some enterprise and analytic applications, but in the era of industry 4.0, the performance of cloud technologies is expected to improve particularly following the security in both network application and host levels [135]. The main companies behind cloud computing development and deployment are Amazon, Microsoft, Google, and IBM. These cloud providers often implement inflexible pricing schemes for cloud users based on the duration [139].
AI is the knowledge-based and thinking program coded and designed in machines to imitate human or animal reasoning ability [155]. For the past few years, AI has been applied in complex operations such as drilling fluid, underground mining [156, 157], and maintenance, as well as monitoring of sophisticated manufacturing systems [158]. The emerging AI applications that are currently shaping industry 4.0 journey include self-driving cars, human speech and face recognition, and interpreting of complex data and medicines, for example, cardiovascular medicine) [159]. As we move to industry 4.0, AI advancement gear towards integration of AI technology with other technologies such as Big Data, cloud computing to perform gigantic tasks, and to widen their application in all fields. For example, a recent finding indicates that AI can be properly applied to handle infectious disease Big Data analytics in healthcare sectors [160]. Notable companies behind AI development include Google, SpaceX, Apple, GE, and Microsoft [159, 161].
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