Content Delivery Networks Research Articles

E-Commerce Website Hosting Using AWS Cloud Services

The primary extent of this task is to comprehend how to best use different distributed computing administrations to get great execution for less expense. Amazon surrenders a marvelous arrangement of web helps that enables fashioners to make dynamic and generous applications. Conveying on AWS can save you time, money and work diverged from building and staying aware of additional customary frameworks. Amazon Web Services offers a wide plan of overall cloud-based things including process, limit, informational collections, assessment, coordinating, flexible, engineer gadgets, the board contraptions, IoT, security, and try applications: on- demand, open quite promptly, with pay-all the more just as expenses emerge esteeming. From data warehousing to association instruments, lists to content transport, more than 200 AWS organizations are available. New organizations can be provisioned quickly, without the blunt capital expense. Today, AWS gives a significantly trustworthy, flexible, negligible cost establishment stage in the cloud that controls a colossal number of associations in 190 countries all over the planet. This licenses adventures, new organizations, little and medium-sized associations and clients in the public region to get to the construction blocks they need to answer quickly to changing business essentials. Amazon EC2 (Elastic Compute Cloud), VPC (Virtual Private Cloud), IAM (Identity And Access Management), Cloud Watch, CDN (Content Delivery Network) and Route 53 are used here since it is very popular in giving extraordinary disseminated figuring organizations. In this report, I'll tell you, step by step, through the procedure of using the AWS Marketplace to game plan and produce another AWS Cloud server. In this task we furthermore used progression gadgets like GIT and Jenkins for robotization.

Strategy for improving the football teaching quality by AI and metaverse-empowered in mobile internet environment

The metaverse has influenced the development of science and technology since it was proposed. While artificial intelligence (AI) is still one of the important technologies to solve problems in metaverse. The success of AI in various fields and the development of information technology make the integration of sports industry and AI an inevitable trend. Currently, the traditional physical education is experiencing qualitative changes, and the demand of integrating AI and metaverse into physical education is becoming more and more obvious. Football teaching is an important part of physical education, and Virtual Reality (VR) has the characteristics of immersion, interaction and imagination, which can build virtual and realistic football teaching process. In this study, through 360-degree panoramic VR football teaching videos empowered by the metaverse and K-means algorithm based on machine learning under AI, we study the strategies for improving the quality of football teaching in the mobile Internet environment. Therefore, we propose K-means based optimized 360-degree panoramic VR football teaching video delivery strategy. In addition, we conduct simulation experiments under content delivery networks simulator, and the simulation results show that the proposed strategy is superior to the baselines in terms of proxy server hit ratio, byte hit ratio, mean response time and students quality of experience. Moreover, through the 360-degree panoramic VR football teaching videos learning, students can intuitively analyze the actions and improve the teaching quality. The reconstruction of football teaching environment is beneficial to promote the combination of football teaching and smart learning.

A Vehicle–Ground Integration Information Network Scheme Based on Small Base Stations

The transmission bandwidth of a vehicle–ground connection is low when an EMU (electric multiple unit) is running in a high-speed scenario. To this end, this paper focuses on the need to solve the problem of the poor bandwidth of the vehicle–ground integration information network, and proposes a vehicle–ground integration information network scheme for EMUs based on small base stations. Based on the existing wi-fi system of the EMU, in order to realize the coverage of the 5G signal in the carriage, this paper—through the deployment of the technical characteristics of 5G—sinks the customized UPF (user plane function) and MEC (mobile edge computing) to the train carriage, and expands the internet channels of the train carriage. Relying on the technologies of MEC and CDN (content delivery network) for high-speed railways, network extension service products can satisfy passengers’ needs around network rate and delay. On the one hand, this can relieve the pressure of the network backhaul and save the bandwidth resources of the vehicle–ground integration information network. On the other hand, it can help operators to reduce the investment of network construction, operation, and maintenance. As a result, the proposed scheme can inspire the products that match the extended service needs of the passenger, realize the technical and innovation value of the 5G mobile network, and achieve business model innovation in high-speed mobile scenarios.

An Approximation Algorithm for Joint Caching and Recommendations in Cache Networks

Streaming platforms, like Netflix and YouTube, strive to offer high streaming quality (SQ), in terms of bitrate, delays, etc., to their users. Meanwhile, a significant share of content consumption of these platforms is heavily influenced by recommendations. In this setting, the user's overall experience is a product of both the user's interest in a recommended content, i.e., the recommendation quality (RQ), and the SQ of this content. However, network decisions (like caching) that affect the SQ are usually made without considering the recommender's actions. Likewise, recommendations are chosen independently of the potential delivery quality. In this paper, we define a metric of streaming experience (MoSE) that captures the fundamental tradeoff between the SQ and RQ. We aim to jointly optimize caching and recommendations in a generic network of caches, with the objective of maximizing this metric. This is in line with the recent trend for content providers to simultaneously act as Content Delivery Network owners, implying that the same entity may handle both caching and recommendation decisions. We formulate this joint optimization problem and prove that it can be approximated up to a constant factor. To the best of our knowledge, this is the first polynomial algorithm to achieve a constant approximation ratio for the joint problem. Moreover, our numerical experiments show important performance gains of our algorithm over baseline schemes and existing algorithms in the literature.

User Mapping Strategy in Multi-CDN Streaming: A Data-Driven Approach

Using Content Delivery Networks (CDNs) for video distribution has become the de facto approach for video streaming today, because they are easy to use (e.g., video chunks can be delivered as files via HTTP) and have good scalability. Today, it has become the norm rather than the exception for video providers to hire multiple CDNs for their video services in a pay-per-use manner—not only to serve users at different locations, but also to reduce operational costs. Given that multiple CDNs and their peering servers exist at many different locations, selecting different CDNs for different users in the same online video system has become a critical decision that can significantly affect the users’ quality of experience (QoE). Conventional strategies are generally rule-based, e.g., assigning users to CDNs according to their locations or ISPs, but cannot guarantee any particular QoE level because QoE is affected by a combination of complicated factors. In this paper, we propose using a data-driven approach to study the factors determining users’ QoE, including both quality of service (QoS) and user factors. Our findings indicate that QoE is affected by both the QoS provided by the CDNs and user preferences for the video content. We design a machine learning-based predictive model to capture the “utility” of a video for a user given a particular QoS guarantee. Based on that, we strategically assign CDNs to users to maximize the overall QoE. One month trace-driven experiments are used to demonstrate the effectiveness and efficiency of our design.

Content delivery network for secure of software defined networking by using IPv4, OpenFlow, and ALTO

Software defined networking is a programmability function network by easiness for maintenance and configuration. The administrators of network can change the traffic rules during the commuting process. SDN is an arising network structure with programmability and centralization and this leads to introduce potential security concerns. Though the TLS ability support secure for control plane but computationally aggravating and complex to configure as well as not compatible with OpenFlow protocol. For this reason, a content delivery network can be used to increase the ability of network services dynamically and automatically. In order that relieve the threat we proposed architecture for SDN depending on CDN. In our proposed architecture, we use application layer traffic optimization (ALTO) protocol to be as servers enable mapping for the network to produce a summarized vision. We also hide the identity of the forwarding devices by take advantage of IPv4 and OpenFlow transaction identification fields into the control packets through implement of two authentication structures via efficient Salsa20 stream cipher. Finally, the work results explain the proposed architecture can efficiently eliminate of attack types and provide more detectability to attackers.

The Impact of Cloud-Based Live Streaming Technologies on Mobile Applications: Development and Future Trends

The proliferation of cloud-based live streaming technologies has significantly impacted the development and functionality of mobile applications. These technologies, leveraging scalable cloud infrastructure, offer developers a robust platform for delivering real-time streaming content, improving user engagement, and expanding service capabilities. This paper explores the transformative effects of cloud-based live streaming on mobile application development, emphasizing key innovations, current challenges, and future trends. Cloud-based live streaming technologies enable mobile applications to handle high-resolution video streams with minimal latency, thanks to advanced cloud infrastructure and Content Delivery Networks (CDNs). The elasticity and scalability of cloud services allow for dynamic adjustment of resources based on user demand, ensuring a seamless viewing experience across diverse network conditions. This has led to significant improvements in video quality and accessibility, making live streaming more reliable and engaging for end-users.

Image DePO: Towards Gradual Decentralization of Online Social Networks using Decentralized Privacy Overlays

Centralized online social networks --- e.g., Facebook, Twitter and TikTok --- help drive social connection on the Internet, but have nigh unfettered access to monitor and monetize the personal data of their users. This centralization can especially undermine the use of the social internet by minority populations, who disproportionately bear the costs of institutional surveillance. We introduce a new class of privacy-enhancing technology --- decentralized privacy overlays (DePOs) --- that helps cOSN users regain some control over their personal data by allowing them to selectively share secret content on cOSNs through decentralized content distribution networks. As a first step, we present an implementation and user evaluation of Image DePO, a proof-of-concept design probe that allows users to upload and share secret photos on Facebook through the Interplanetary File System peer-to-peer protocol. We qualitatively evaluated Image DePO in a controlled, test environment with 19 queer and Black, Indigenous, (and) Person of Color (BIPOC) participants. We found that while Image DePO could help address the institutional threats with which our participants expressed concern, interpersonal threats were the more salient concern in their decisions to share content. Accordingly, we argue that in order to see widespread use, DePOs must align protection against abstract institutional threats with protection against the more salient interpersonal threats users consider when making specific sharing decisions.

Smart Communication and Security by Key Distribution in Multicast Environment

The service providers are aiming to provide multicast applications, primarily in the area of content delivery and secure wireless networks, due to the increased adoption of network systems and demand for secured wireless networks communication. Cryptography enables the users to send information across insecure networks using data encryption and decryption with key management. The research paper proposes a unique way of safeguarding network systems using cryptographic keys, as well as a fuzzy-based technique for improving security by reducing symmetric and asymmetric key overhead. To enable efficient communication, fuzzy-based rules with security triads and cryptographic key management methods are used. When the key distribution is decentralized, security implementation becomes more difficult, and multiple types of attacks are possible. Fuzzy logic-based key management methods are used in addition to offering a novel technique for secure cryptography systems. The novelty of the work is that the simulation work is also carried out to verify the data in on-demand distance vector (AODV) multicast wireless routing that supports 100 nodes with network performance parameters such as delay, control overhead, throughput, and packet delivery ratio. The system supports up to 128-bit key embedded with 128-bit plain data in cryptographic encryption and decryption.

Fast Data Access through Nearest Location-Based Replica Placement

Nowadays, millions of telecommunication devices, IoT sensors, and web services, especially social media sites, are producing big data every second. Such applications with massive data-generating capabilities need to access these data quickly. Among other approaches, cloud computing provides content delivery networks, which utilize data replication for better latency to such real-time applications. Fast processing, storage, and timely analysis of these data are the challenge for most of these future Internet applications. However, cloud computing is the ultimate storage and processing paradigm to resolve such issues and to deal with big data, with the same speed in which data are being produced. Moreover, cloud computing technology has been evolved with the tag line of “Everything as A Service” now, and for powering all these services, provision of data is a compulsory and significant task. To provide end user with easy and fast access to data, the cloud maintains backup and replicates the copies in multiple data centers. The geographical locations of these data centers where data are being placed have a profound impact on data access time. To deal with the challenge of effective data replication and to reduce data access time to a minimum, we propose a genetic algorithm (GA)-based technique to suggest and store data on nearly located data centers. The proposed algorithm improves the access time, and thus, the efficiency of cloud servers by providing quality of service (QoS) to end user.

Cache management in content delivery networks using the metadata of online social networks

The number of requests on content delivery networks (CDN) originating from the online social networks (OSN) by sharing the content weblink increases according to recorded data. The sequence of the OSN originated requests shows temporal burstiness with a typical interval shorter than the ordinary requests. We consider CDN and OSN as a multilayer network and exploit the average spreading power of each user in the OSN to predict the temporal pattern of the corresponding consecutive social requests that may originate from this user to improve the underlying cache management mechanism. The traditional least recently used (LRU) content replacement algorithm uses the statistical popularity of contents to increase the cache’s hit ratio. We propose LRU-Social, which defers the eviction of social requests for a specific amount of time to take advantage of the possible burstiness in the underlying interval without missing the popular contents’ hits. We model the content link sharing by the susceptible–infected–recovered (SIR) spreading process in the underlying OSN to compute the user spreading power. We provide numerical studies for synthetic streams consisting of ordinary requests that follow Zipf’s popularity model and social requests to justify the effectiveness of the LRU-Social compared to the LRU.

A Distributed Real-Time Transcoding CDN System Design and Performance Simulation Study.

Content Delivery Network (CDN) technology is one of the core technologies for performance optimization of mobile WEB applications, but it often encounters bottlenecks in video storage. In this paper, based on studying the basic technical model of distributed real-time transcoding CDN, we propose an overall technical architecture of a distributed real-time transcoding CDN system, present the business process of distributed real-time transcoding CDN resource negotiation in the model, design a resource storage policy model and algorithm, including the hot storage ratio resource storage value evaluation method and storage resource allocation algorithm. Through simulation experiments, we verify the technical efficiency of the distributed real-time transcoding CDN system by simulation experiments.

Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

Ensuring Profit and QoS When Dynamically Embedding Delay-Constrained ICN and IP Slices for Content Delivery

Content Delivery Networks (CDNs) are becoming more critical due to the tremendous growth of video traffic. This paper proposes a complete framework targeting the creation of Information Centric Network (ICN) and IP slices for content delivery. Leveraging ICN's in-network caching advantages, our solution is tailored to a VNF placement context where ICN and IP slices are dynamically created over a physical infrastructure. Embedding delay-constrained slices with high profits requires adjustments of the available resources based on the traffic and slice demands, which leads to the reassigning of the slices. However, slice reassignment may lead to additional costs, content disruption in service delivery, and QoS violation. We formulate the problem as an Integer Linear Programming (ILP) and propose a cost-efficient dynamic network slicing heuristic. The objective is to maximize the profit of infrastructure providers while meeting the different QoS requirements of the slices. Our evaluation validates the effectiveness of profit maximization and analyzes the impact of QoS violation on infrastructure provider profit. The results are compared to a recent work from the literature and an optimal solution. The evaluation shows that our proposed heuristic is promising and offers a near-optimal solution while improving profitability and ensuring QoS.

Consumer Electronic Devices: Evolution and Edge Security Solutions

The pervasive growth in the variety of consumer electronic devices (CEDs), as well as the Internet traffic, has evolved various security risks toward their usage. This article presents the concept of communications without a centralized server to facilitate the fastest communication between end-devices, e.g., user handheld devices, and edge devices. Three communication scenarios are presented to support authentication in the proposed architecture. To understand the concept, this article first describes the growth in the CEDs market. Afterward, the discussion on various challenges faced for incorporating security in such devices is presented along with edge-based security solutions. Finally, the article presents a use-case of the content delivery network to discuss various security solutions based on the edge with the hope that these solutions will work for future security.

Multi-neural network based tiled 360°video caching with Mobile Edge Computing

It is challenging to stream 360° videos over the mobile network for its stringent latency and high bandwidth requirements. Although edge-based viewport adaptive tiled 360° video streaming solutions alleviate the bandwidth demand, the backhaul congestion and low latency concern remain persistent when data is served from the Content Delivery Network over the Internet. Edge caching can help mitigate these issues by storing the content at the edge of the cellular networks on the base station. However, caching 360° videos is challenging because of the large file size, which is further convoluted by tile selection in caching decisions. In this work, we propose a Mobile Edge Computing (MEC) based tiled 360° caching solution that uses Long–Short-Term-Memory (LSTM) and Convolutional Neural Network (CNN) in conjunction to address the challenges associated with 360° video caching. Specifically, the LSTM model predicts the future popularity of the videos, assisting in cache replacement decisions. For the selected videos, the CNN model, which is trained using the saliency map of the video, identifies the most engaging tiles in the videos for caching using the video content itself. The caching of tiles instead of the whole 360° videos improves the caching efficiency of the resource-constrained MEC server. The LSTM model is optimized based on the loss value of different hyperparameters, and AUROC (Ares Under ROC Curve) is used to evaluate the accuracy of the CNN model. Both the models produce highly accurate results. The results from extensive simulations show that the proposed solution significantly outperforms the existing methods. It improves the cache hit rate by at least 10% and reduces the backhaul usage by at least 35% with significant improvement in end-to-end latency, which is crucial for the quality of experience in 360° video streaming.

Intelligent resource management at the network edge using content delivery networks

ABSTRACT In a distributed denial-of-service attack, a large volume of packets is sent to the victim server to prevent service delivery and make it unavailable to valid users and clients at the network edge. In this paper, we use content delivery networks to propose a new intelligent solution that protects the main servers at the network edge by using alternative edge servers and agents. Thus, Internet attacks on the main servers could be prevented. The simulation results show that, the proposed mechanism has considerably increased the processing efficiency at the edge servers, and simultaneously provided the required security against internet attacks.

Technical analysis of content placement algorithms for content delivery network in cloud

<p>Content placement algorithm is an integral part of the cloud-based content de-livery network. They are responsible for selecting a precise content to be re-posited over the surrogate servers distributed over a geographical region. Although various works are being already carried out in this sector, there are loopholes connected to most of the work, which doesn't have much disclosure. It is already known that quality of service, quality of experience, and the cost is always an essential objective targeting to be improved in existing work. Still, there are various other aspects and underlying reasons which are equally important from the design aspect. Therefore, this paper contributes towards reviewing the existing approaches of content placement algorithm over cloud-based content delivery network targeting to explore open-end re-search issues.</p>

Towards client-side active measurements without application control

Monitoring performance and availability are critical to operating successful content distribution networks. Internet measurements provide the data needed for traffic engineering, alerting, and network diagnostics. While there are significant benefits to performing end-user active measurements, these capabilities are limited to a small number of content providers with application control. In this work, we present a solution to the long-standing problem of issuing active measurements from clients without requiring application control, e.g., injecting JavaScript to the content served. Our approach uses server-side programmable features of the Network Error Logging specification that allow a CDN to induce a browser connection to an HTTPS server of the CDN's choosing without application control.

Fleeting neutrality: The inadequacies of the ex-ante net neutrality regulations in India

The paper seeks to address the economic and fundamental inadequacies of ex-ante net neutrality regulations in India. The current net neutrality regulations consider the internet service provider (ISP) market in a two-sided spectrum of content and application providers (CAPs) and end-users. However, the rapidly growing internet space has undergone numerous developments in recent years, giving rise to complexities such as vertical partnerships between the ISPs, CAPs and OTT service providers, the use of content delivery networks (CDNs) for the last mile, deployment of 5G on mobile devices, etc. The main hypothesis is that the ex-ante net neutrality regulations lack the tools to combat modern problems with their antiquated approach and measures. Accordingly, to analyse this proposition, the paper considers the existent literature on the fundamental aspects of net neutrality norms, and juxtaposes the economic and empirical findings from a post‐net neutrality regulation stance. The findings reveal that net neutrality measures that prohibit paid-prioritisation and zero-rating services lead to economic inefficiencies. Further, the restrictive net neutrality measures hamper investment and innovation in the market and fall short in dealing with vertical integration and the use of CDNs. Hence, to promote competition and protect net neutrality, especially in the lower layers of the ISP market, the paper recommends measures such as digital dynamism and transparency rules and studies the role of ex-post competition regulations in aiding net neutrality.