1,536 research outputs found
Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
The fifth generation of cellular networks (5G) will rely on edge cloud
deployments to satisfy the ultra-low latency demand of future applications. In
this paper, we argue that such deployments can also be used to enable advanced
data-driven and Machine Learning (ML) applications in mobile networks. We
propose an edge-controller-based architecture for cellular networks and
evaluate its performance with real data from hundreds of base stations of a
major U.S. operator. In this regard, we will provide insights on how to
dynamically cluster and associate base stations and controllers, according to
the global mobility patterns of the users. Then, we will describe how the
controllers can be used to run ML algorithms to predict the number of users in
each base station, and a use case in which these predictions are exploited by a
higher-layer application to route vehicular traffic according to network Key
Performance Indicators (KPIs). We show that the prediction accuracy improves
when based on machine learning algorithms that rely on the controllers' view
and, consequently, on the spatial correlation introduced by the user mobility,
with respect to when the prediction is based only on the local data of each
single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin
Fog computing : enabling the management and orchestration of smart city applications in 5G networks
Fog computing extends the cloud computing paradigm by placing resources close to the edges of the network to deal with the upcoming growth of connected devices. Smart city applications, such as health monitoring and predictive maintenance, will introduce a new set of stringent requirements, such as low latency, since resources can be requested on-demand simultaneously by multiple devices at different locations. It is then necessary to adapt existing network technologies to future needs and design new architectural concepts to help meet these strict requirements. This article proposes a fog computing framework enabling autonomous management and orchestration functionalities in 5G-enabled smart cities. Our approach follows the guidelines of the European Telecommunications Standards Institute (ETSI) NFV MANO architecture extending it with additional software components. The contribution of our work is its fully-integrated fog node management system alongside the foreseen application layer Peer-to-Peer (P2P) fog protocol based on the Open Shortest Path First (OSPF) routing protocol for the exchange of application service provisioning information between fog nodes. Evaluations of an anomaly detection use case based on an air monitoring application are presented. Our results show that the proposed framework achieves a substantial reduction in network bandwidth usage and in latency when compared to centralized cloud solutions
A Decade of Research in Fog computing: Relevance, Challenges, and Future Directions
Recent developments in the Internet of Things (IoT) and real-time
applications, have led to the unprecedented growth in the connected devices and
their generated data. Traditionally, this sensor data is transferred and
processed at the cloud, and the control signals are sent back to the relevant
actuators, as part of the IoT applications. This cloud-centric IoT model,
resulted in increased latencies and network load, and compromised privacy. To
address these problems, Fog Computing was coined by Cisco in 2012, a decade
ago, which utilizes proximal computational resources for processing the sensor
data. Ever since its proposal, fog computing has attracted significant
attention and the research fraternity focused at addressing different
challenges such as fog frameworks, simulators, resource management, placement
strategies, quality of service aspects, fog economics etc. However, after a
decade of research, we still do not see large-scale deployments of
public/private fog networks, which can be utilized in realizing interesting IoT
applications. In the literature, we only see pilot case studies and small-scale
testbeds, and utilization of simulators for demonstrating scale of the
specified models addressing the respective technical challenges. There are
several reasons for this, and most importantly, fog computing did not present a
clear business case for the companies and participating individuals yet. This
paper summarizes the technical, non-functional and economic challenges, which
have been posing hurdles in adopting fog computing, by consolidating them
across different clusters. The paper also summarizes the relevant academic and
industrial contributions in addressing these challenges and provides future
research directions in realizing real-time fog computing applications, also
considering the emerging trends such as federated learning and quantum
computing.Comment: Accepted for publication at Wiley Software: Practice and Experience
journa
Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities
(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksHan, G.; Guizani, M.; Lloret, J.; Chan, S.; Wan, L.; Guibene, W. (2017). Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities. IEEE Communications Magazine. 55(12):16-17. https://doi.org/10.1109/MCOM.2017.8198795S1617551
Will SDN be part of 5G?
For many, this is no longer a valid question and the case is considered
settled with SDN/NFV (Software Defined Networking/Network Function
Virtualization) providing the inevitable innovation enablers solving many
outstanding management issues regarding 5G. However, given the monumental task
of softwarization of radio access network (RAN) while 5G is just around the
corner and some companies have started unveiling their 5G equipment already,
the concern is very realistic that we may only see some point solutions
involving SDN technology instead of a fully SDN-enabled RAN. This survey paper
identifies all important obstacles in the way and looks at the state of the art
of the relevant solutions. This survey is different from the previous surveys
on SDN-based RAN as it focuses on the salient problems and discusses solutions
proposed within and outside SDN literature. Our main focus is on fronthaul,
backward compatibility, supposedly disruptive nature of SDN deployment,
business cases and monetization of SDN related upgrades, latency of general
purpose processors (GPP), and additional security vulnerabilities,
softwarization brings along to the RAN. We have also provided a summary of the
architectural developments in SDN-based RAN landscape as not all work can be
covered under the focused issues. This paper provides a comprehensive survey on
the state of the art of SDN-based RAN and clearly points out the gaps in the
technology.Comment: 33 pages, 10 figure
Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology : Future Perspectives
The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.Peer reviewe
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