Content Delivery Networks Research Articles

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.
 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

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

<span>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.</span>

Competitive Analysis of Online Elastic Caching of Transient Data in Multi-Tiered Content Delivery Network

Competitive Analysis of Online Elastic Caching of Transient Data in Multi-Tiered Content Delivery Network

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


 
 
 
 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.
 
 
 

QIoTChain: Quantum IoT‐blockchain fusion for advanced data protection in Industry 4.0

AbstractThe modern world has its foundation built on data. Data is generated all the time, and many aspects of the society and economy are built upon it. In today's world, data is employed in better decision‐making, innovation, and improving the efficiency of systems. It becomes essential to protect it from being tampered with or misused. Encryptions based on cryptographic systems are used in mainstream data protection these days. There have been newer iterations of mechanisms based on new techniques using blockchain and the zero trust model and even systems that employ fog computing along with content delivery networks. The advent of computation through quantum technologies also helped in developing data security with post‐quantum mechanisms. The critical conditions for the working of these mechanisms hinder their adoption primarily. This also leads current systems to lack one or more of these technologies. Thus, the importance of a unified system that can employ quantum technologies effectively becomes prominent and requires a definitive investigation. In this article, a Quantum‐IoT‐based data protection scheme that can be of value to Industry 4.0 is analyzed. Light is also shed on innovations in quantum computing that can help solve not just current problems but also the future.

Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access

BackgroundDistributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data. MethodsWSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN). ResultsA total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection “Whole Slide Images as Non-fungible Tokens Project” on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API. ConclusionWhole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.

Can We Save the Public Internet?

The goal of this short document is to explain why recent developments in the Internet's infrastructure are problematic. As context, we note that the Internet was originally designed to provide a simple universal service - global end-to-end packet delivery - on which a wide variety of end-user applications could be built. The early Internet supported this packet-delivery service via an interconnected collection of commercial Internet Service Providers (ISPs) that we will refer to collectively as the "public Internet." The Internet has fulfilled its packet-delivery mission far beyond all expectations and is now the dominant global communications infrastructure. By providing a level playing field on which new applications could be deployed, the Internet has enabled a degree of innovation that no one could have foreseen. To improve performance for some common applications, "enhancements" such as caching (as in content-delivery networks) have been gradually added to the Internet. The resulting performance improvements are so significant that such enhancements are now effectively necessary to meet current content delivery demands. Despite these tangible benefits, this document argues that the way these enhancements are currently deployed seriously undermines the sustainability of the public Internet and could lead to an Internet infrastructure that reaches fewer people and is largely concentrated among only a few large-scale providers. We wrote this document because we fear that these developments are now decidedly tipping the Internet's playing field towards those who can deploy these enhancements at massive scale, which in turn will limit the degree to which the future Internet can support unfettered innovation. This document begins by explaining our concerns but goes on to articulate how this unfortunate fate can be avoided. To provide more depth for those who seek it, we provide a separate addendum with further detail.