243 research outputs found
Efficient Proactive Caching for Supporting Seamless Mobility
We present a distributed proactive caching approach that exploits user
mobility information to decide where to proactively cache data to support
seamless mobility, while efficiently utilizing cache storage using a congestion
pricing scheme. The proposed approach is applicable to the case where objects
have different sizes and to a two-level cache hierarchy, for both of which the
proactive caching problem is hard. Additionally, our modeling framework
considers the case where the delay is independent of the requested data object
size and the case where the delay is a function of the object size. Our
evaluation results show how various system parameters influence the delay gains
of the proposed approach, which achieves robust and good performance relative
to an oracle and an optimal scheme for a flat cache structure.Comment: 10 pages, 9 figure
Social Cooperation for Information-Centric Multimedia Streaming in Highway VANETs
Abstract-High-quality multimedia streaming services in Vehicular Ad-hoc Networks (VANETs) are severely hindered by intermittent host connectivity issues. The Information Centric Networking (ICN) paradigm could help solving this issue thanks to its new networking primitives driven by content names rather than host addresses. This unique feature, in fact, enables native support to mobility, in-network caching, nomadic networking, multicast, and efficient content dissemination. In this paper, we focus on exploring the potential social cooperation among vehicles in highways. An ICN-based COoperative Caching solution, namely ICoC, is proposed to improve the quality of experience (QoE) of multimedia streaming services. In particular, ICoC leverages two novel social cooperation schemes, namely partner-assisted and courier-assisted, to enhance information-centric caching. To validate its effectiveness, extensive ns-3 simulations have been executed, showing that ICoC achieves a considerable improvement in terms of start-up delay and playback freezing with respect to a state-of-the-art solution based on probabilistic caching
Caching as an Image Characterization Problem using Deep Convolutional Neural Networks
Caching of popular content closer to the mobile user can significantly
increase overall user experience as well as network efficiency by decongesting
backbone network segments in the case of congestion episodes. In order to find
the optimal caching locations, many conventional approaches rely on solving a
complex optimization problem that suffers from the curse of dimensionality,
which may fail to support online decision making. In this paper we propose a
framework to amalgamate model based optimization with data driven techniques by
transforming an optimization problem to a grayscale image and train a
convolutional neural network (CNN) to predict optimal caching location
policies. The rationale for the proposed modelling comes from CNN's superiority
to capture features in grayscale images reaching human level performance in
image recognition problems. The CNN is trained with optimal solutions and
numerical investigations reveal that the performance can increase by more than
400% compared to powerful randomized greedy algorithms. To this end, the
proposed technique seems as a promising way forward to the holy grail aspect in
resource orchestration which is providing high quality decision making in real
time.Comment: 7 pages, 5 figure
Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges
International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
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