Content Delivery Services Research Articles

Fine-Grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks

The 5 G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5 G UDNs. In this paper, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76 m, with up to 80% of the unknown areas.

Using DynaRules to Integrate Context Information in Semantic Correlation Rules for Intelligent Content Delivery

In the past, information deliveries in technical communication were often considered as a normative duty, but has now grown into a knowledge-bearing and knowledge-transmitting medium by more modern approaches of dynamic content delivery. As described in a previous publication [1] implementing and working with semantic correlation rules and knowledge graphs enhances, for example, the contextual understanding of information and the technology of content delivery. The use of various established media technologies, which are explained in detail in this paper, prevents risks such as unclear information management and a lack of transparency. Companies must ensure that necessary information is provided in a simple and targeted manner where it is needed. Therefore, using an efficient system which provides detailed information about products, processes or services, a company is able to create, manage and deliver a high amount of information in a professional way. In an era where efficiency, innovation and speed set the tone, technologies have been developed on the market for creating and delivering content. In this paper we will moreover describe the possible interplay of recent IT architectures, non-linear DynaRules of semantic models and AI extensions to enable metadata-linked content effectively with reduced fuzziness according to the regarded context of intelligent content delivery and information services.

QoS-Aware Content Dissemination Based on Integrated Social and Physical Attributes Among Cellular and V2V Users

With the recent advancement in intelligent transportation systems (ITS), the vehicular network has become the major force in content dissemination, and it is essential to improve efficiency in content delivery and quality and service (QoS). The rapid growth in vehicular data traffic and limited resources in the vehicular network have created a bottleneck in content dissemination. In addition, the demand of high data rate, low latency and user's satisfaction on content make content dissemination more challenging. To address the above problem which involves vehicle to vehicle (V2V) peer discovery and resource allocation, we propose an efficient content dissemination scheme which integrates both social and physical attributes of the users. V2V users are paired to share content based on the similarity in their preferences and link reliability, while considering the QoS of vehicle to network (V2N) users in both uplink and downlink phases. Moreover, we present an improved matching algorithm for channel allocation, which maximizes the sum rate of V2V pairs weighted by the intensity of their social relationship. Simulation results demonstrate the performance of the proposed scheme in terms of weighted sum rate and user's satisfaction on content.

Coded multicasting for content delivery over predictable time-varying satellite communication networks

With the development of astronautic technology, communication satellites now have a tremendous gain in both quantity and quality, and have already shown their capability on multi-functional converged communication other than telecommunication. Under this circumstance, increasing the transmission efficiency of satellite communication network becomes a top priority. In this paper, we focus on content delivery service on satellite networks, where each ground station may have prefetched some file fragments. We cast this problem into a coded caching framework so as to exploit the coded multicast gain for minimizing the satellite communication load. We first propose an optimization-based coded multicast scheme by considering the special property that the satellite network topology is predictable and time-variant. Then, a greedy based fast algorithm is proposed, which can tremendously reduce the computation complexity with a small loss in optimality. Simulation experiments conducted on two Walker constellation satellite networks show that our proposed coded multicast method can efficiently reduce the communication load of satellite networks.

Secure Content Delivery for Connected and Autonomous Trucks: A Coalition Formation Game Approach

With the ever-increasing demand for the content delivery services in autonomous vehicular networks (AVNs), caching popular contents in the edge nodes in advance is expected to reduce the transmission delay. Current works on the contents cached in connected and autonomous vehicles (CAVs) or roadside units (RSUs) are facing the problems of limited caching size and high deployment cost. In this paper, by exploiting the advantages of high caching space and flexibility of truck platoons composed of connected and autonomous trucks (CATs), we propose a secure content delivery service for CATs based on coalition formation game. Firstly, in order to protect the security and privacy of content delivery services, a differential privacy model is proposed to protect the sensitive information of CATs. Meanwhile, the differential privacy model is combined with the incentive based trust evaluation model to monitor the behaviors of CATs. In the incentive based models, CATs are encouraged to improve their trust values to obtain higher utilities and find a balance between confidence levels and utilities. Moreover, a coalition formation game is established among CATs with the same driving route, in which all CATs can maximize their utilities with the formation of several minor coalitions. Finally, we conduct extensive simulations to demonstrate the effectiveness and superiority of the proposed scheme.

CHANNEL MODELING OF THE OTH OPTICAL TRANSMISSION SYSTEM WITH QUADRATURE AMPLITUDE MODULATION

The rapid development of services for providing broadband access to the Internet and multimedia content delivery services required the widespread use of fiber optic transmission systems (FOTS) for the transmission of digital traffic. The transmitter signal passes through an optical line, which includes optical fiber and optical amplifiers. In the optical fiber, linear and nonlinear distortions are introduced into the signal, and optical amplifiers generate additive noise of amplified spontaneous emission. In this paper, we develop a model of the optical channel of OTH system with QAM modulation, which takes into account linear and nonlinear distortions of the optical propagation medium and additive noise of the amplified spontaneous radiation of optical amplifiers. To obtain accurate results, the method of simulation modeling was used. The block diagram of the developed model contains a transmitter, an optical line and a coherent receiver of the optical signal. The expression for the time form of the electric field intensity of the optical signal at the transmitter output is given. Modeling of the optical fiber was performed by the split step Fourier method. The noise of the amplified spontaneous emission of optical amplifiers was formed in the frequency domain by means of the Fourier transform operation. The quality of the optical channel was estimated by calculating the average value of the Q-factor of the optical signal at the receiver output. The dependence of the Q-factor from the number of sections of the optical line and the power level of the optical signal was obtained. It is shown that to maximize the quality characteristics of the optical channel, the optimal value of the signal power should be used. The comparison of the obtained results with the data from specialized software products is carried out. Sufficient accuracy of the proposed model was demonstrated. It is recommended to use the developed channel model in the design of new and modernization of existing fragments of networks based on OTH systems with QAM modulation.

Opportunistic offloading scheme for content delivery service using electro‐mobility networks

AbstractVehicular caching (VC) in electro‐mobility networks has become promising for supporting the needs of low end‐to‐end service delays and reducing the load of networks (i.e. edge caching [EC]) for content delivery service. However, since VCs are available only when they are in the vicinity of the user, intermittent connectivity should be considered. In this paper, the opportunistic offloading scheme for content delivery service is proposed, and a decision is made regarding whether to use VC or EC for the content delivery service. In the proposed scheme, when VC is not available, the user determines whether to choose VC or EC for the content download. If VC is chosen, the user delays the content download to increase the VC utilization according to the expectation of VC contacts. This results in the delayed service completion but can reduce the EC load (i.e. offloading gain). If EC is chosen, the user can download the content without any delay or service interruption. To find the optimal policy, this paper formulates a Markov decision process (MDP) problem and uses the value iteration algorithm to solve the problem. The evaluation results show that the proposed scheme outperforms the comparison schemes based on the expected reward.

Delay-Aware Content Delivery With Deep Reinforcement Learning in Internet of Vehicles

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

An Online Model Viewer for Cultural Heritage in Unity 3D

We present a prototype of a 3D model viewer for cultural heritage built in Unity 3D. The Unity engine allows for the use of custom shaders that can increase realism and provide a uniquely immersive experience with cultural heritage artifacts. Examples of custom shaders that this architecture supports include image-based specular reflections and polynomial texture mapping (PTM) for translucent materials. Furthermore, the software can be built and deployed as a stand-alone WebGL application viewable in the browser without relying on third-party content delivery services. The code for this project is open source and available on GitHub.

Multimedia content delivery services in the cloud with partial sleep and abandonment

Multimedia content delivery services in the cloud with partial sleep and abandonment

AI-Aided Integrated Terrestrial and Non-Terrestrial 6G Solutions for Sustainable Maritime Networking

The maritime industry is experiencing a technological revolution that affects shipbuilding, operation of both seagoing and inland vessels, cargo management, and working practices in harbors. This ongoing transformation is driven by the ambition to make the ecosystem more sustainable and cost-efficient. Digitalization and automation help achieve these goals by transforming shipping and cruising into a much more cost- and energy-efficient and decarbonized industry segment. The key enablers in these processes are always-available connectivity and content delivery services, which can not only aid shipping companies in improving their operational efficiency and reducing carbon emissions, but also contribute to enhanced crew welfare and passenger experience. Due to recent advancements in integrating high-capacity and ultra-reliable terrestrial and non-terrestrial networking technologies, ubiquitous maritime connectivity is becoming a reality. To cope with the increased complexity of managing these integrated systems, this article advocates the use of artificial intelligence and machine-learning-based approaches to meet the service requirements and energy efficiency targets in various maritime communications scenarios.

Mapping Applications Intents to Programmable NDN Data-Planes via Event-B Machines

Location-agnostic content delivery, in-network caching, and native support for multicast, mobility, and security are key features of the novel named data networks (NDN) paradigm. NDNs are ideal for hosting content-centric next-generation applications such as Internet of things (IoT) and virtual reality. Intent-driven management is poised to enhance the performance of the offered NDN services to these applications while reducing its management complexity. This article proposes I2DN, intent-driven NDN, a novel architecture that aims at realizing the first step towards intent modeling and mapping to data-plane configurations for NDNs. In I2DN, network operators and application developers express their abstract and declarative content delivery and network service goals and constraints using uttered or written intents. The intents are classified using built-in intent templates, and a slot filling procedure identifies the semantics of the intent. We then employ Event-B machine (EBM) language modeling to represent these intents and their semantics. The resulting EBMs are then gradually refined to represent configurations at the NDN programmable data-plane. The advantages of the proposed adoption of EBM modeling are twofold. First, EBMs accurately capture the desired behavior of the network in response to the specified intents and automatically refine it into concrete configurations. Second, EBM’s formal verification property, referred to as its proof obligation, ensures that the desired properties of the network or its services, as defined by the intent, remain satisfied by the refined EBM representing the final data-plane configurations. Experimental evaluation results demonstrate the feasibility and efficiency of our proposed work.

Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

With the development of internet of vehicles, the traditional centralized content caching mode transmits content through the core network, which causes a large delay and cannot meet the demands for delay-sensitive services. To solve these problems, on basis of vehicle caching network, we propose an edge collaborative caching scheme. Road side unit (RSU) and mobile edge computing (MEC) are used to collect vehicle information, predict and cache popular content, thereby provide low-latency content delivery services. However, the storage capacity of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time. Through content sharing, collaborative caching can relieve the storage burden on caching servers. Therefore, we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching (MVECC) scheme, so as to realize the collaborative caching among cloud, edge and vehicle. MVECC uses deep reinforcement learning to predict what needs to be cached on RSU, which enables RSUs to cache more popular content. In addition, MVECC also introduces a mobility-aware caching replacement scheme at the edge network to reduce redundant cache and improving cache efficiency, which allows RSU to dynamically replace the cached content in response to the mobility of vehicles. The simulation results show that the proposed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.

Novel Architecture of Content Delivery Network Based on Big Data for Power Saving

This paper proposes an architecture of content delivery network (CDN) based on big data for power saving. There are two types of video content: hot content and cold content. When video content is accessed frequently, it is called hot content. Conversely, when video content is accessed infrequently, it is called cold content. In CDN, there is an origin server and a CDN cache server. A CDN cache server has a replicated content and provides its content to the end users nearby. Therefore, the user can receive the requested content from the closest proximity for fast content. The proposed architecture in this paper powers off the cold content server in CDN cache server when the number of cold content requests decreases. Hence, the proposed architecture for content delivery services based on power saving is expected to be useful for providing multimedia streaming services with low power consumption for content providers.

On Multi-Resource Procurement in Internet Access Markets

With the increasing popularity and significance of content delivery services, especially video streaming, stringent Quality of Service (QoS) requirements have been placed upon Internet content providers (CPs). As a result, CPs have strong incentives to pay for resources such as premium peering bandwidth and cache capacity that ameliorate service quality. In this paper, we study an Internet access market with multiple access resources that CPs can purchase to enhance service quality. We build a detailed content delivery model, through which we characterize the traffic throughput and the resulting utilities of CPs and social welfare. We show that a market equilibrium exists for a multi-resource market, at which the optimization problems for individual utilities and social welfare coincide. Furthermore, we characterize the optimal purchasing strategies of CPs as well as how varying parameters are going to change the market equilibrium via sensitivity analysis.

The NECOS Approach to End-to-End Cloud-Network Slicing as a Service

Cloud-network slicing is a promising approach to serve vertical industries delivering their services over multiple administrative and technological domains. However, there are numerous open challenges to provide end-to-end slices due to complex business and engineering requirements from service and resource providers. This article presents a reference architecture for the cloud-network slicing concept and the practical realization of the slice-as-a-service paradigm, which are key results from the Novel Enablers in Cloud Slicing (NECOS) project. The NECOS platform has been designed to consider modularity, separation of concerns, and multi-domain dynamic operation as prime attributes. The architecture comprises a set of interworking components to automatically create, manage, and decommission end-to-end cloud-network slice instances in a lightweight manner. NECOS orchestrates slices at runtime, spanning across core/edge data centers and wired/wireless network infrastructures. The novelties of the multi-domain NECOS platform are validated through three proof-of-concept experiments: (i) a touristic content delivery service slice deployment featuring on-demand virtual infrastructure management across three countries on different continents to meet particular slice requirements; (ii) intelligent slice elasticity driven by machine learning techniques; and (iii) market-place-based resource discovery capabilities.

Delay-Aware Caching in Internet-of-Vehicles Networks

With the emergence of a large number of computational resource-intensive applications and various content delivery services, there is an explosion of data growth in the Internet of Vehicles (IoV). To improve the transmission performance of the IoV, caching content on the edge of the network is considered as a potential solution to reduce the content transmission delay. In this article, we investigate the content caching decisions optimization method in the IoV to minimize the content fetching delay for vehicles, which is based on the vehicle-to-vehicle (V2V) collaboration. A delay-aware content caching (DCC) algorithm in the IoV is proposed, which consists of vehicle associations, content caching, and precaching decisions optimization. First, a delay-aware vehicle associations (DVAs) algorithm is proposed to optimize the vehicle associations. Consequently, based on the vehicle associations results, the content caching decisions are optimized in two network scenarios according to the existence of the handover vehicles. Finally, a practical scenario of Shanghai with time-varying traffic flow is used for simulations and the effectiveness of the proposed DCC algorithm is verified.

Managing Hybrid Learning Model: Web Enhanced Course Media in Physics Learning

This research was study about managing hybrid learning model: web enhanced course media in physics learning. The research conducted in SMA Negeri 2 Pare-pare and XI grade MIPA Class were chosen as study object. The study method is modified research and developing model which use to manage Hybrid learning media. The study result show that (1) web enhanced course media is suitable to develop by using LMS based website, (2) web enhanced course media have to have four service components i.e (1) administration, (2) assessment, (3) content delivery, and (4) community service, and (4) the implementation of web enhanced course media makes the student physics learning motivation gain to high grade and increase the number of student which interest in physics.

DEVS-based experimental framework for blockchain services

Blockchain works according to the principles of distributed transactions and processing tiny hash-based blocks. Data located in the blockchain preserves its integrity and transparency, which enables participants to verify it. Today, people apply blockchain technology in domains that must maintain the integrity of the data. However, people can be reluctant to introduce blockchain technology due to the complexity of the technology and the resultant inefficiency. This work presents a DEVS-based experimental methodology for performance evaluation of blockchain-based services. The experimental methodology is tested by implementing a content delivery service by utilizing the Ethereum blockchain framework. As the evaluation result, we show the overall turnaround time of the service and investigate the delay of smart contract function consisting the service. In addition, the evaluation result shows the difference in performance between blockchain-based and database-based services. This work presents a methodology for evaluating blockchain service performance and shows experimental results that can be referenced by people who are planning to implement blockchain-based services.

Collaborative Reinforcement Learning Based Route Planning for Cloud Content Delivery Networks

Cloud Content Delivery Networks (CCDNs) scale their content delivery services by taking advantage of the flexible cloud resources and management mechanisms so that users can get the content nearby and reduce traffic of the central network. The content delivery efficiency of CCDNs and quality of service can be improved by rationally planning routing. However, the existing routing planning strategies are not suitable for CCDNs because the number of nodes and the network traffic in CCDNs are dynamic. In this paper, we propose a routing planning method that considers the influence of these factors. In our method, we first build a new framework called the collaborative reinforcement learning based CCDN to capture the dynamic characteristics of the above factors through the $V$ -value feedback model, and we quantify the node transmission efficiency with the $Q$ value. Based on this framework, we propose a collaborative reinforcement learning based delivery tree construction algorithm to obtain a delivery tree. Through numerical simulation, it is found that the delivery path constructed by this method can effectively improve the efficiency of content delivery.