32 research outputs found
Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices
Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications
Duration-adaptive Video Highlight Pre-caching for Vehicular Communication Network
Video traffic in vehicular communication networks (VCNs) faces exponential
growth. However, different segments of most videos reveal various
attractiveness for viewers, and the pre-caching decision is greatly affected by
the dynamic service duration that edge nodes can provide services for mobile
vehicles driving along a road. In this paper, we propose an efficient video
highlight pre-caching scheme in the vehicular communication network, adapting
to the service duration. Specifically, a highlight entropy model is devised
with the consideration of the segments' popularity and continuity between
segments within a period of time, based on which, an optimization problem of
video highlight pre-caching is formulated. As this problem is non-convex and
lacks a closed-form expression of the objective function, we decouple multiple
variables by deriving candidate highlight segmentations of videos through
wavelet transform, which can significantly reduce the complexity of highlight
pre-caching. Then the problem is solved iteratively by a highlight-direction
trimming algorithm, which is proven to be locally optimal. Simulation results
based on real-world video datasets demonstrate significant improvement in
highlight entropy and jitter compared to benchmark schemes
Performance Evaluation of Optimized Predictive Model for Software Defined Network Traffic Management using Machine Learning
Communication channel is essential in any type of engagement for delivering and receiving data via the internet. To determine the most efficient and safe way through which network data may travel while minimizing the danger of network breaches or cyber-attacks. The objective is to build an optimized network traffic management predictive model that can predict the ideal path in real-time while accounting through the dynamic nature of software defined network traffic and the continuously changing danger of landscaping. To design a robust model of the data and scalable system that can suggest accurate suggestions of route to the network managers, a thorough grasp of network’s infrastructure, data analysis, and machine learning techniques are applied. Choosing the optimum path route data from the sdn based network traffic dataset, the model suggests an optimal path to avoid network communication traffic and congestion. Here nine Machine Learning algorithms are explored and analysed their performance by using the percentage split, resampling and cross validation which originally recorded as 92.76% and after training with cross validation it improved to 98.40% providing the best optimal path with minimum congestions. Building the optimized network traffic management model not only provide network security but also contribute to environmental sustainability. Their capacity to properly filter and manage network traffic helps to decrease energy usage by predicting the optimal routes for software defined network traffic
Time-Shifted Prefetching and Edge-Caching of Video Content: Insights, Algorithms, and Solutions
Video traffic accounts for 82% of global Internet traffic and is growing at an unprecedented rate. As a result of this rapid growth and popularity of video content, the network is heavily burdened. To cope with this, service providers have to spend several millions of dollars for infrastructure upgrades; these upgrades are typically triggered when there is a reasonably sustained peak usage that exceeds 80% of capacity. In this context, with network traffic load being significantly higher during peak periods (up to 5 times as much), we explore the problem of prefetching video content during off-peak periods of the network even when such periods are substantially separated from the actual usage-time. To this end, we collected YouTube and Netflix usage from over 1500 users spanning at least a one-year period consisting of approximately 8.5 million videos collectively watched. We use the datasets to analyze and present key insights about user-level usage behavior, and show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. Thereafter, equipped with the datasets and our derived insights, we develop a set of data-driven prediction and prefetching solutions, using machine-learning and deep-learning techniques (specifically supervised classifiers and LSTM networks), which anticipates the video content the user will consume based on their prior watching behavior, and prefetches it during off-peak periods. We find that our developed solutions can reduce nearly 35% of peak-time YouTube traffic and 70% of peak-time Netflix series traffic. We developed and evaluated a proof-of-concept system for prefetching video traffic. We also show how to integrate the two systems for prefetching YouTube and Netflix content. Furthermore, based on our findings from our developed algorithms, we develop a framework for prefetching video content regardless of the type of video and platform upon which it is hosted.Ph.D
Conhecimento da mobilidade do consumidor em redes centradas em informação
Mobile data traffic is expanding significantly since the surge and evolution of wireless
communication technologies, leading to the design and implementation of
different types of mobile networks.
Information Centric Network paradigms have been pointed as an alternative to
bypass the restrictions imposed by the traditional IP Networks, such as the one
imposed by the mobility of its users. Despite their potential advantages regarding
mobile wireless environments, several significant research challenges remain to be
addressed, more specifically the communication damage due to handover, causing
loss of packets.
The scope of this dissertation is the development of NDN-based mechanisms with
support for Consumer mobility in two different communication approaches: single
content request and publish-subscribe. The proposed schemes address a remote
mobility predictor entity, whose purpose is to monitor and anticipate trajectories,
while compelling the infrastructure to adjust to the new paths, resulting in an
efficient way to manage the consumers’ mobility with the purpose of attaining a
better quality of service to users.
The implementation and evaluation of the proposed schemes were performed using
ndnSIM, through functional and non-functional scenarios. The latter uses
real traces of urban mobility and connectivity. The obtained results show that
the proposed solution far surpasses the native NDN workflow and the traditional
publish-subscribe solutions with respect to content delivery ratio and network overhead.O tráfego de dados móveis tem vindo a crescer significativamente, sobretudo devido
à evolução das tecnologias de comunicação sem fios, o que tem vindo a implicar o
desenho e implementação de novos e diferentes tipos de redes móveis.
Os paradigmas de redes centradas em informação têm sido apontados como uma
alternativa para contornar as restrições impostas pelas redes tradicionais IP, nomeadamente
a mobilidade dos seus utilizadores. Apesar das potenciais vantagens em
relação aos ambientes móveis sem fios, vários desafios de investigação ainda necessitam
de ser resolvidos, mais especificamente aqueles relacionados com o processo
de handover dos seus utilizadores móveis, levando por vezes à perda de informação.
Esta dissertação tem como objetivo o desenvolvimento de mecanismos de suporte à
mobilidade do Consumidor para redes ICN, utilizando duas abordagens distintas de
comunicação: solicitação única de conteúdo e o modelo publish − subscribe. Os
esquemas propostos exploram uma entidade remota de previsão de mobilidade, cujo
objetivo é monitorizar e antecipar eventuais trajetórias de posição dos utilizadores
móveis, obrigando a infraestrutura a ajustar-se aos novos caminhos do consumidor,
resultando numa forma eficiente de gestão de mobilidade dos utilizadores com o
objetivo de garantir uma melhor qualidade de serviço.
A implementação e avaliação dos esquemas propostos foi realizada utilizando o
ndnSIM, em cenários funcionais e não funcionais. Estes últimos utilizam registos
reais de mobilidade e conetividade urbana. Os resultados obtidos mostram
que a solução proposta ultrapassa significativamenta a versão nativa do NDN e as
soluções tradicionais de publish − subscribe, considerando a taxa de entrega de
conteúdos e sobrecarga da rede.Mestrado em Engenharia de Computadores e Telemátic
Multi-Dimensional Resource Orchestration in Vehicular Edge Networks
In the era of autonomous vehicles, the advanced technologies of connected vehicle lead to the development of driving-related applications to meet the stringent safety requirements and the infotainment applications to improve passenger experience. Newly developed vehicular applications require high-volume data transmission, accurate sensing data collection, and reliable interaction, imposing substantial constrains on vehicular networks that solely rely on cellular networks to fetch data from the Internet and on-board processors to make driving decisions. To enhance multifarious vehicular applications, Heterogeneous Vehicular Networks (HVNets) have been proposed, in which edge nodes, including base stations and roadside units, can provide network connections, resulting in significantly reduced vehicular communication cost. In addition, caching servers are equipped at the edge nodes, to further alleviate the communication load for backhaul links and reduce data downloading delay. Hence, we aim to orchestrate the multi-dimensional resources, including communication, caching, and sensing resources, in the complex and dynamic vehicular environment to enhance vehicular edge network performance. The main technical issues are: 1) to accommodate the delivery services for both location-based and popular contents, the scheme of caching contents at edge servers should be devised, considering the cooperation of caching servers at different edge nodes, the mobility of vehicles, and the differential requirements of content downloading services; 2) to support the safety message exchange and collective perception services for vehicles, communication and sensing resources are jointly allocated, the decisions of which are coupled due to the resource sharing among different services and neighboring vehicles; and 3) for interaction-intensive service provisioning, e.g., trajectory design, the forwarding resources in core networks are allocated to achieve delay-sensitive packet transmissions between vehicles and management controllers, ensuring the high-quality interactivity. In this thesis, we design the multi-dimensional resource orchestration schemes in the edge assisted HVNets to address the three technical issues.
Firstly, we design a cooperative edge caching scheme to support various vehicular content downloading services, which allows vehicles to fetch one content from multiple caching servers cooperatively. In particular, we consider two types of vehicular content requests, i.e., location-based and popular contents, with different delay requirements. Both types of contents are encoded according to fountain code and cooperatively cached at multiple servers. The proposed scheme can be optimized by finding an optimal cooperative content placement that determines the placing locations and proportions for all contents. To this end, we analyze the upper bound proportion of content caching at a single server and provide the respective theoretical analysis of transmission delay and service cost (including content caching and transmission cost) for both types of contents. We then formulate an optimization problem of cooperative content placement to minimize the overall transmission delay and service cost. As the problem is a multi-objective multi-dimensional multi-choice knapsack one, which is proved to be NP-hard, we devise an ant colony optimization-based scheme to solve the problem and achieve a near-optimal solution. Simulation results are provided to validate the performance of the proposed scheme, including its convergence and optimality of caching, while guaranteeing low transmission delay and service cost.
Secondly, to support the vehicular safety message transmissions, we propose a two-level adaptive resource allocation (TARA) framework. In particular, three types of safety message are considered in urban vehicular networks, i.e., the event-triggered message for urgent condition warning, the periodic message for vehicular status notification, and the message for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, the proposed TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmission for each vehicle group at the first level, and the group is formed by a set of neighboring vehicles. To learn the implicit relation between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, the SRA scheme offline optimizes the allocation of sensing resources (i.e., choosing vehicles to provide perception data) and communication resources. With the resource reservation result for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real time. Extensive simulation results are provided to demonstrate the effectiveness of the proposed TARA framework in terms of the high successful reception ratio and low latency for message transmissions, and the high quality of collective environmental perception.
Thirdly, we investigate forwarding resource sharing scheme to support interaction intensive services in HVNets, especially for the delay-sensitive packet transmission between vehicles and management controllers. A learning-based proactive resource sharing scheme is proposed for core communication networks, where the available forwarding resources at a switch are proactively allocated to the traffic flows in order to maximize the efficiency of resource utilization with delay satisfaction. The resource sharing scheme consists of two joint modules: estimation of resource demands and allocation of available resources. For service provisioning, resource demand of each traffic flow is estimated based on the predicted packet arrival rate. Considering the distinct features of each traffic flow, a linear regression scheme is developed for resource demand estimation, utilizing the mapping relation between traffic flow status and required resources, upon which a network switch makes decision on allocating available resources for delay satisfaction and efficient resource utilization. To learn the implicit relation between the allocated resources and delay, a multi-armed bandit learning-based resource sharing scheme is proposed, which enables fast resource sharing adjustment to traffic arrival dynamics. The proposed scheme is proved to be asymptotically approaching the optimal strategy, with polynomial time complexity. Extensive simulation results are presented to demonstrate the effectiveness of the proposed resource sharing scheme in terms of delay satisfaction, traffic adaptiveness, and resource sharing gain.
In summary, we have investigated the cooperative caching placement for content downloading services, joint communication and sensing resource allocation for safety message transmissions, and forwarding resource sharing scheme in core networks for interaction intensive services. The schemes developed in the thesis should provide practical and efficient solutions to manage the multi-dimensional resources in vehicular networks
Fundamental Limits of Caching: Symmetry Structure and Coded Placement Schemes
Caching is a technique to reduce the communication load in peak hours by prefetching contents
during off-peak hours. In 2014, Maddah-Ali and Niesen introduced a framework for coded
caching, and showed that significant improvement can be obtained compared to uncoded caching. Considerable efforts have been devoted to identify the precise information theoretic fundamental limit of such systems, however the difficulty of this task has also become clear. One of the reasons for this difficulty is that the original coded caching setting allows multiple demand types during delivery, which in fact introduces tension in the coding strategy to accommodate all of them. We seek to develop a better understanding of the fundamental limit of coded caching.
In order to characterize the fundamental limit of the tradeoff between the amount of cache
memory and the delivery transmission rate of multiuser caching systems, various coding schemes have been proposed in the literature. These schemes can largely be categorized into two classes, namely uncoded prefetching schemes and coded prefetching schemes. While uncoded prefetching schemes in general over order-wise optimal performance, coded prefetching schemes often have better performance at the low cache memory regime. At first sight it seems impossible to connect these two different types of coding schemes, yet finding a unified coding scheme that achieves the optimal memory-rate tradeoff is an important and interesting problem. We take the first step on this direction and provide a connection between the uncoded prefetching scheme proposed by Maddah Ali and Niesen (and its improved version by Yu et al.) and the coded prefetching scheme proposed by Tian and Chen. The intermediate operating points of this general scheme can in fact provide new memory-rate tradeoff points previously not known to be achievable in the literature. This new general coding scheme is then presented and analyzed rigorously, which yields a new inner bound to the memory-rate tradeoff for the caching problem.
While studying the general case can be difficult, we found that studying the single demand type
systems will provide important insights. Motivated by these findings, we focus on systems where the number of users and the number of files are the same, and the demand type is when all files are being requested. A novel coding scheme is proposed, which provides several optimal memory transmission operating points. Outer bounds for this class of systems are also considered, and their relation with existing bounds is discussed.
Outer-bounding the fundamental limits of coded caching problem is difficult, not only because
there are tons of information inequalities and problem specific equalities to choose from, but also because of identifying a useful subset (and often a quite small subset) from them and how to combine them to produce an improved outerbound is a hard problem. Information inequalities can be used to derive the fundamental limits of information systems. Many information inequalities and problem-specific constraints are linear equalities or inequalities of joint entropies, and thus outer bounding the fundamental limits can be viewed as and in principle computed through linear programming. However, for many practical engineering problems, the resultant linear program (LP) is very large, rendering such a computational approach almost completely inapplicable in practice. We provide a method to pinpoint this reduction by counting the number of orbits induced by the symmetry on the set of the LP variables and the LP constraints, respectively. We proposed a generic three-layer decomposition of the group structures for this purpose. This general approach can also be applied to various other problems such as extremal pairwise cyclically symmetric entropy inequalities and the regenerating code problem.
Decentralized coded caching is applicable in scenarios when the server is uninformed of the
number of active users and their identities in a wireless or mobile environment. We propose a
decentralized coded prefetching strategy where both prefetching and delivery are coded. The proposed strategy indeed outperforms the existing decentralized uncoded caching strategy in regimes of small cache size when the numbers of files is less than the number of users. Methods to manage the coding overhead are further suggested
Towards Smart Vehicular Environments via Deep Learning and Emerging Technologies
Intelligent Transportation Systems (ITS) embrace smart vehicular environments through a fully connected paradigm known as vehicular networks. Vehicular networks allow automobiles to stay online and connected with their surroundings while travelling. In that sense, vehicular networks enable various activities; for example, autonomous driving, road surveillance, data collection, content delivery, and many others. This leads to more efficient, safer, and comfort driving experiences and opens up new opportunities for many business sectors. As such, the networking industry and academia have shown great interests in advancing vehicular networks and leveraging relevant services.
In this dissertation, several vehicular network problems are addressed along with proposing novel ideas and utilizing effective solutions. As opposed to stationary or slow moving communications, vehicular networks experience more challenging environment as a result of vehicle mobility. Consequently, vehicular networks suffer from ever-changing topology, short contact times, and intractable propagation environments. In particular, this dissertation presents six works that participate in supplementing the literature as follows. First, a content delivery framework in the context of vehicular network is studied where digital contents are generated by different content providers (CP) and have distinct values. To this end, a prefetching technique along with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications are used to enable fast content delivery. Furthermore, a pricing model is proposed to deal with contents' values to attain a satisfactory Quality of Experience (QoE). Second, a more advanced system model is discussed to cache contents with the assistance of vehicles and to enable a disconnected and fixed Road-Side Unit (RSU) to participate in providing content delivery services. The changing popularity of contents is investigated besides accounting for the limited RSU cache capabilities. Third, the stationary RSU proposed in the second work is replaced by a more flexible infrastructure, namely an aerial RSU mounted on an unmanned aerial vehicle (UAV). The mobility of the UAV and its constrained energy capacity are analyzed and Deep Reinforcement Learning is incorporated to aid in solving the challenges in leveraging UAVs. Fourth, the previous two studies are integrated by investigating the collaboration between a UAV and terrestrial RSUs in delivering large-size contents. A strategy to fill up the UAV cache is also suggested via mulling contents over vehicles. Fifth, the complexity of vehicular urban environments is addressed. In particular, the problem of disconnected areas in vehicular environments due to the appearance of high-rise buildings and other obstacles is studied. In details, a Reconfigurable Intelligent Surface (RIS) is exploited to provide indirect links between the RSU and vehicles travelling through such areas. Our sixth and final contribution deals with time-constrained Internet of Things (IoT) devices (IoTD) supporting ITS networks. In this regard, a UAV is dispatched to collect their data timely and fully while being assisted by a RIS to improve the wireless channel quality. In the end, this dissertation provides discussions that highlight open research directions worth of further investigations
Mobile Ad-Hoc Networks
Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks
A review on green caching strategies for next generation communication networks
© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching