539 research outputs found
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Artificial intelligent system for multimedia services in smart home environments
[EN] Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially founded by the Universitat Polite`cnica de Vale`ncia through the postdoctoral PAID-10-20 program.Rego Mañez, A.; Gonzalez Ramirez, PL.; Jimenez, JM.; Lloret, J. (2022). Artificial intelligent system for multimedia services in smart home environments. Cluster Computing. 25(3):2085-2105. https://doi.org/10.1007/s10586-021-03350-zS2085210525
A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet
Future integrated terrestrial-aerial-satellite networks will have to exhibit
some unprecedented characteristics for the provision of both communications and
computation services, and security for a tremendous number of devices with very
broad and demanding requirements in an almost-ubiquitous manner. Although 3GPP
introduced the concept of self-organization networks (SONs) in 4G and 5G
documents to automate network management, even this progressive concept will
face several challenges as it may not be sufficiently agile in coping with the
immense levels of complexity, heterogeneity, and mobility in the envisioned
beyond-5G integrated networks. In the presented vision, we discuss how future
integrated networks can be intelligently and autonomously managed to
efficiently utilize resources, reduce operational costs, and achieve the
targeted Quality of Experience (QoE). We introduce the novel concept of
self-evolving networks (SENs) framework, which utilizes artificial
intelligence, enabled by machine learning (ML) algorithms, to make future
integrated networks fully intelligent and automated with respect to the
provision, adaptation, optimization, and management aspects of networking,
communications, and computation. To envisage the concept of SEN in future
integrated networks, we use the Intelligent Vertical Heterogeneous Network
(I-VHetNet) architecture as our reference. The paper discusses five prominent
communications and computation scenarios where SEN plays the main role in
providing automated network management. Numerical results provide an insight on
how the SEN framework improves the performance of future integrated networks.
The paper presents the leading enablers and examines the challenges associated
with the application of SEN concept in future integrated networks
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