Content Delivery Networks Research Articles

TrafAda: Cost-Aware Traffic Adaptation for Maximizing Bitrates in Live Streaming

The business growth of live streaming causes expensive bandwidth costs from the Content Delivery Network service. It necessitates traffic adaptation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , adapting video bitrates for cost-efficient bandwidth utilization, especially under the 95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>th</i> </sup> percentile pricing. However, our data-driven investigations indicate the existing methods are hard to achieve bitrate-cost balance in a long month-level billing cycle due to dynamic traffic patterns. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> , a learning-based cost-aware traffic adaptation method consisting of i) an ultra-long-term bandwidth demand forecasting model to learn complex bandwidth usage patterns, and ii) an imitation learning-based bitrate decision mechanism to optimize the ultra-long-term objective. We have implemented and deployed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> on a large-scale live streaming system in China serving over one billion viewers from 388 cities. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> improves peak-hour bitrate, quality of experience (QoE), and watching time by 34.75%, 44.56%, and 10.68%, respectively, without extra bandwidth cost, which can be converted to a considerable value for a commercial system.

A Novel Approach to Improve Quality of Service in Cloud-Based Content Delivery Network

A content delivery network (CDN) improves normal website functions and increases customer satisfaction. In addition, CDNs foster a seamless online experience, ensuring reliable performance and bolstering cybersecurity measures for enhanced user trust and data protection. It improves High-speed content delivery, Real-time streaming, Multi-user scaling etc. One of the most notable achievements is the remarkable reduction in latency, thanks to advanced CDN technologies that position content closer to end-users, enhancing responsiveness and making real- time applications, online gaming, and interactive content more enjoyable. Furthermore, cost optimization has been a focal point, with CDNs efficiently managing resources and data transfer to deliver content while keeping operational expenses in check. In essence, the ongoing enhancements in CDNs underscore their pivotal role in shaping the digital content landscape. With a focus on speed, scalability, latency reduction, cost efficiency, security, global reach, and data- driven decision-making, CDNs have become indispensable for organizations seeking to excel in serving an increasingly diverse and demanding global audience. In a world where digital content reigns supreme, these improvements empower organizations to meet and exceed user expectations, contributing to their overall success in the digital age. Keywords: Content Delivery Network, Cloud-based Content Delivery Network, Quality metrics and monitoring, Resource management, Performance optimisation.

Energy efficiency of cache collaboration in core content distribution networks

With escalating demands for high-definition video, cache collaboration allows neighbor nodes to share locally stored content in order to reduce download traffic. High energy consumption associated with content delivery remains a concern for Content Distribution Networks (CDNs). Therefore, this paper proposes cluster-based collaborative caching in a core network employing IP over WDM. The aim is to allow sets of core caches to fully share content while minimizing power. A Mixed Integer Linear Programming (MILP) model is used to form energy-efficient cache clusters. The energy consumption of the network is evaluated under different cluster sizes to find the optimum size that minimizes energy. To evaluate the influence of content popularity distribution, a heavy-tailed Zipf distribution and an Equal popularity distribution are evaluated. In addition, the work investigates the influence of downlink traffic behavior and power consumption parameters on optimum cluster sizes. Attained results reveal that maximum savings in energy consumption introduced by cluster-based collaborative caching are up to 34.3% and 21.8% under the Zipf and Equal distribution, respectively. Cache collaboration is not recommended when all core nodes contain fully replicated content servers. Results also show that power consumption parameters do not influence cluster formation. It is recommended keeping cache collaboration in the core network simple, so as to reduce intra-cluster communication.

MOODLE MANUAL UPGRADE METHODOLOGY

This abstract outlines a systematic four-step methodology for upgrading Moodle from version 3.6.5 to 3.9.9. The process begins with comprehensive backup and preparation, followed by the replacement of the Moodle codebase and execution of the upgrade script. Post-upgrade checks are performed to ensure the smooth functioning of core and third-party components. Additionally, the abstract explores the actions and functions of the cron script, emphasizing its role in executing scheduled tasks critical for Moodle's operation.&#x0D; Furthermore, the document sheds light on common reasons behind slow Moodle operation and provides practical tips for enhancing system speed. Recommendations include optimizing server resources, database maintenance, strategic caching implementation, and the judicious use of content delivery networks (CDNs). The importance of upgrading PHP and database versions is highlighted, alongside the benefits of disabling unused features and employing server monitoring tools. The abstract emphasizes the significance of staying abreast of Moodle updates for continued performance improvements and bug fixes, offering a holistic guide for upgrading and optimizing Moodle systems.

A novel fuzzy programming approach for piece selection problem in P2P content distribution network.

Piece selection policy in dynamic P2P networks play crucial role and avoid the last piece problem. BitTorrent uses rarest-first piece selection mechanism to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. The problem of piece section is multiple objectives. A novel fuzzy programming approach is introduced in this article to solve the multiple objectives piece selection problem in P2P network, in which some of the factors are fuzzy in nature. Piece selection problem has been prepared as a fuzzy mixed integer goal programming piece selection problem that includes three primary goals such as minimizing the download cost, time, maximizing speed and useful information transmission subject to realistic constraints regarding peer's demand, capacity and dynamicity. The proposed approach has the ability to handle practical situations in a fuzzy environment and offers a better decision tool to each peer to select optimal pieces to download from other peers in dynamic P2P network. Extensive simulations are carried out to demonstrate the effectiveness of the proposed model. It is proved that proposed system outperforms existing with respect to download cost, time and meaningful exchange of useful information.

Beyond 1 Million Nodes—Crowdsourced Video CDN: Architecture, Technology, and Economy

The video service has already become one of the Internet killer applications. To deliver the ever growing video traffic with the Quality of Experience (QoE) guarantee, deploying the infrastructure of Content Delivery Network (CDN) closer and closer to users has been a noticeable trend. And this trend becomes more promising when recently CDNs providers start utilizing the storage and network resources of the edge network devices (e.g., the Wi-Fi Access Points) for content delivery [13], i.e., crowdsourced CDN. In this article, we envision the crowdsourced CDN and propose a set of practical strategies to guide the implementation of such new paradigm. Firstly, we perform a large-scale measurement to explore the geo-content popularity and the geo-heterogeneity of the resource distribution. Benefiting from the measurement observations, we design the key solutions for the system operation, including regional content popularity prediction based on Markov Decision Process (MDP), region partition for the edge device organization and the collaborative content replication. Moreover, we consider the economic issues and propose a sequential auction mechanism to cope with the resource allocation when multiple Content Providers (CPs) compete for the edge resources for their content caching. We also demonstrate the performance gain of our design with data-driven simulation.

Modeling the Location Selection of Mirror Servers in Content Delivery Networks

Modeling the Location Selection of Mirror Servers in Content Delivery Networks

Cloud Computing Based Digital Media Content Distribution Technology

The rapid development of digital media content and the increasing demand of users for high-quality content have made digital media content distribution technology more and more important in practical applications. Cloud computing, as an infrastructure, has the advantages of flexible scalability and high quality for storage, management and distribution of digital media content. However, more in-depth research is needed to understand the impact of content distribution strategies under different network environments and media types. In this paper, based on the cloud computing platform, a series of comparative experiments were designed to evaluate the performance of cloud computing based digital media content distribution technology from different aspects. Factors considered in this paper included different network types, bandwidth, latency, packet loss rate, media types (video, audio, image) and distribution strategies. The experimental data is collected and analyzed, and the key indicators such as download time and playback success rate are evaluated. After analyzing the experimental results, we find that the performance of digital media content distribution is significantly affected by different network environments and media types. Cloud computing based content distribution strategies have been proved to achieve the effect of fast download and high success playback rate. The response time of cloud based distribution strategies is up to 4.7 seconds, and the bandwidth utilization rate reaches 89%, and the minimum latency is 104 ms. Under the same conditions, the performance of cloud computing is generally better than that of Content Delivery Network (CDN) and Peer-to-Peer (P2P). This study is a comprehensive experimental study of cloud computing based digital media content distribution technology. It has in-depth understanding of the performance of network environment, media types and distribution strategies. These research results have important reference and guidance to optimize digital media content distribution technology and improve the user experience.

Bayesian K-means clustering based quality of experience aware multimedia video streaming

&lt;span&gt;Media streaming is an essential approach for delivering multimedia information from the source distributor to the end-user through the Internet. Along with the development of more number of users and the spread of mobile devices, the availability and diversity of multimedia applications has also increased. Multimedia users primarily prioritize quality of experience (QoE), as they seek to access multimedia content with high availability and enjoy smooth video streaming in the shortest possible time. The impact of video delivery plays a significant role in QoE, which is efficiently made by delivering the content through a specialized content delivery network architecture. In this research, a Bayesian K-means clustering algorithm is proposed for the identification of QoE in multimedia video streaming. In this multimedia video streaming, the Amazon Prime video dataset is utilized for determining the performance of the proposed model. The proposed method is developed from the ‘Patching Up’ the video quality problem (PatchVQ) model, the from patches to pictures (PaQ-2-PiQ) model is utilized for the spatial feature extraction, and 3D ResNet-18 is utilized for temporal feature extraction. The proposed Bayesian K-means achieved a QoE reward function of 5,237.42 and 5841.36 as well as a fairness reward function of 5,841.36 and 8,732.08 at the speed of 1,500 kB/s and 2,000 kB/s respectively.&lt;/span&gt;

A case study of content delivery networks for the CMS ex-periment

In 2029 the LHC will start the high-luminosity LHC program, with a boost in the integrated luminosity resulting in an unprecedented amount of ex- perimental and simulated data samples to be transferred, processed and stored in disk and tape systems across the worldwide LHC computing Grid. Content de- livery network solutions are being explored with the purposes of improving the performance of the compute tasks reading input data via the wide area network, and also to provide a mechanism for cost-effective deployment of lightweight storage systems supporting traditional or opportunistic compute resources. In this contribution we study the benefits of applying cache solutions for the CMS experiment, in particular the configuration and deployment of XCache serving data to two Spanish WLCG sites supporting CMS: the Tier-1 site at PIC and the Tier-2 site at CIEMAT. The deployment and configuration of the system and the developed monitoring tools will be shown, as well as data popularity studies in relation to the optimization of the cache configuration, the effects on CPU efficiency improvements for analysis tasks, and the cost benefits and impact of including this solution in the region.

Simulation and optimization of Content Delivery Networks considering user profiles and preferences of Internet Service Providers

Simulation and optimization of Content Delivery Networks considering user profiles and preferences of Internet Service Providers

$\varepsilon$-LAP: A Lightweight and Adaptive Cache Partitioning Scheme with Prudent Resizing Decisions for Content Delivery Networks

$\varepsilon$-LAP: A Lightweight and Adaptive Cache Partitioning Scheme with Prudent Resizing Decisions for Content Delivery Networks

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.

Effective Cache Placement for Content Delivery Networks in Fog Computing

Effective Cache Placement for Content Delivery Networks in Fog Computing

Cloud-Based Solutions for Video Streaming and Big Data Testing

In the era of digital transformation, cloud-based solutions have revolutionized the video streaming industry and big data testing methodologies. As consumer demand for high-quality, on-demand video content surges, traditional on-premises infrastructure often falls short in meeting scalability, performance, and cost-efficiency requirements. Cloud-based platforms, with their scalable resources and flexibility, have become essential in addressing these challenges. This paper explores the benefits and implementation strategies of cloud-based solutions for video streaming and big data testing, focusing on how these technologies enhance performance, scalability, and cost-effectiveness Video streaming services require a robust infrastructure capable of handling large volumes of data and delivering seamless user experiences across diverse devices and network conditions. Cloud-based video streaming solutions offer several advantages, including the ability to dynamically scale resources based on user demand, optimize content delivery through content delivery networks (CDNs), and provide a global reach without the need for extensive physical infrastructure. By leveraging cloud services, streaming platforms can reduce latency, enhance video quality, and improve overall user satisfaction. Traditional testing methods often struggle to cope with the complexity and volume of big data. Cloud-based testing platforms provide scalable environments where testers can simulate various data scenarios, execute performance tests, and analyze results with greater efficiency. The cloud's elasticity allows for on-demand provisioning of testing resources, making it easier to handle the dynamic nature of big data and conduct comprehensive testing without investing in expensive hardware.

The Protocol Play: DASHing Ahead or HLSing Back?

This paper presents a comprehensive comparative analysis of two widely adopted adaptive streaming protocols, Dynamic Adaptive Streaming over HTTP (DASH) and HTTP Live Streaming (HLS), within the contemporary landscape of video delivery. Delving into the background of both protocols, the study explores their architectures, media format support, and adaptability to diverse network conditions. A meticulous examination of strengths and weaknesses encompasses performance, scalability, user experience, device compatibility, and implementation ease. The implications for content delivery, including considerations of quality of experience, Content Delivery Network (CDN) compatibility, and cost factors, are thoroughly discussed. The paper also sheds light on current trends, emerging technologies, and the evolving nature of DASH and HLS. In conclusion, the study provides a synthesis of key insights, offering practical recommendations for protocol selection based on specific use cases and contributing valuable perspectives to the dynamic realm of adaptive video streaming.

StarFront: Cooperatively Constructing Pervasive and Low-Latency CDNs Upon Emerging LEO Satellites and Clouds

Internet content providers (ICPs) typically exploit content distribution networks (CDNs) to provide wide-area data access with high availability and low latency. However, our analysis on a large-scale trace collected from seven major CDN operators has revealed that: from a global perspective, there are still a large portion of users suffering from high user-perceived latency due to the insufficient deployment of terrestrial cloud infrastructures, especially in remote or rural areas where even the closest available cache server is too far away. This paper presents, a cost-effective content distribution framework to optimize global CDNs and enable low content access latency anywhere. collaboratively builds CDNs upon emerging low earth orbit (LEO) constellations and existing cloud platforms to satisfy the low latency requirements while minimizing the operational cost. Specifically, exploits a key insight that emerging mega-constellations will consist of thousands of LEO satellites which can be equipped with high-speed data links and storage, and thus can potentially work as “cache in space” to enable pervasive and low-latency data access. judiciously places replicas on either LEO satellite caches or terrestrial cloud caches, and dynamically assigns user requests to proper cache servers based on different constellation parameters, cloud/user distributions and pricing policies. We have implemented a prototype in our testbed, and extensive trace-driven evaluations covering multiple geo-distributed vantage points have demonstrated that can effectively reduce the global content access latency with acceptable operational cost under representative CDN traffic.

ICN-Based Enhanced Content Delivery for CDN

With the rapid growth of internet traffic, the traditional host-to-host TCP/IP architecture is subject to many service limitations faced with content-oriented applications. Various novel network architectures have been proposed to solve these limitations, among which Information-Centric Networking (ICN) is one of the most prominent. ICN features the decoupling of content (service) from the physical devices storing (providing) it through location-independent naming, and offers inherent enhancement to network performance, such as multicast and in-network caching. ICN in-network caching has been extensively studied, and we believe that it may also be the main incentive for ISPs to deploy ICN. A CDN (content delivery network) is a typical content-oriented network paradigm that aims to provide the fast delivery of content. In this paper, we leverage the advantages of the in-network caching of ICN to enhance the content delivery efficiency of CDN by integrating ICN as a service. First, we present our design of a content delivery network enhanced with ICN, called IECDN. Additionally, we formulate a mathematical model to optimize the performance of our proposed design and conduct a series of evaluations. The results indicate that our proposed design provides significant performance gains while reducing bandwidth consumption and shows better resilience to traffic surge.

Towards a software‐defined networking model for consumer‐centric content delivery network for IoT

AbstractEvery entity must communicate seamlessly in an interconnected network. Personalised delivery of information in an open private network(s) among consumers should be transparent while maintaining privacy. In information‐centric content delivery networks (ICDN) or data‐centric content delivery networks (DCDN), data delivery relies heavily on sharing data with third‐party entities, resulting in a privacy breach. To overcome these challenges, this article proposes a novel communication model with a consumer‐centric content delivery network (CCDN) approach for an internet‐of‐things (IoT) network that deals in personalised information delivery with users' identity and location privacy. The article derives motivation from a use case‐based analysis of the proposed communication model (CCDN) by incorporating the latest technologies like software‐defined networks (SDN) and content delivery network (CDN) into IoT Networks to achieve a higher level of personalisation while maintaining user privacy. CCDN leverages SDN‐based CDN for IoT networks in the designed use case that strictly adheres to user‐defined preferences/demands. The article suggests an environment with a possible topology implementation and describes the elements that can help achieve it. The article investigates the need for personalisation and how it is contrary to maintaining user privacy. The article also contributes a survey of the underlying technologies while linking them to IoT.

Designing Secure and Robust E-Commerce Plaform for Public Cloud

&#x0D; &#x0D; &#x0D; &#x0D; The migration of e-commerce platforms to the public cloud has become a pivotal strategy for businesses seeking enhanced scalability, performance, and cost-efficiency. This paper explores the multifaceted design considerations critical to deploying robust e-commerce systems within the public cloud infrastructure. It delves into scalability, ensuring that platforms can handle varying loads with elasticity and grace. Security is examined as a paramount concern, addressing the need for stringent data protection, compliance with industry standards, and the implementation of best practices such as encryption and identity access management. Performance optimization is discussed, with a focus on leveraging content delivery networks and optimizing database operations to ensure swift customer experiences. The paper also covers reliability and availability, emphasizing the necessity of multi-regional deployment and sophisticated disaster recovery plans to guarantee uninterrupted service. Cost management is analyzed, highlighting the importance of understanding cloud pricing models and employing cost-effective resource utilization strategies. Data management is scrutinized, considering secure storage, privacy, and efficient data handling. User experience is identified as a critical component, with personalization and session management being key to customer satisfaction. The role of DevOps and automation in achieving efficient deployment cycles through continuous integration and delivery is outlined. The benefits of a microservices architecture are presented, along with the challenges of managing such distributed systems. Multi-tenancy and isolation are discussed in the context of security and resource optimization. Integration and APIs are explored for their role in facilitating extensibility and seamless third-party service incorporation. Compliance and legal considerations are addressed, underscoring the importance of data sovereignty and regular audits. Lastly, the paper touches on the incorporation of emerging technologies such as AI, ML, and IoT to stay at the forefront of innovation, and concludes with a discussion on environmental considerations for sustainable cloud practices. This comprehensive analysis provides a roadmap for businesses to navigate the complexities of cloud-based e-commerce, ensuring robust, secure, and efficient online retail operations.&#x0D; &#x0D; &#x0D; &#x0D;