Recent Developments in Content Delivery Network: A Survey

Jiaming, Zhao; Liang, Pingjia; Liufu, Weiming; Fan, Zhi‐Ping

doi:10.1007/978-981-15-2767-8_9

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Many excellent surveys [107]- [109], [120] have aimed at the depth of one domain such as routing, deployment and architecture. In the case, our research is in a relatively new field, so except to talk about the related work as the evolutionary, the directions for the exploration from various angles are the focus in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Review on Software Defined Content Delivery Network: A Novel Combination of CDN and SDN

Yang

Pan

2023

IEEE Access

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With the rapid growth of multimedia services, the burden of infrastructure is becoming heavier, which may not guarantee a high-quality experience for users. Whether the multimedia category is video on demand (VOD), live broadcast, or social media is a hot issue. Traditionally, content delivery network (CDN) service providers deploy servers as close to clients as possible to reduce latency. CDN is an overlay network mainly responsible for routing requests, distribution, delivery, and audit. As a supplement to computing and storage capacity, CDN service providers have begun migrating some of their services to the cloud to focus on the delivery process. At the same time, the collaboration between CDN service providers promotes scalability to meet the growing number of requests. However, the underlying physical layer needs to be improved. The cost of the traditional underlying network is very high and cannot be easily expanded. The concept of software definition network (SDN) is proposed to solve these problems. SDN functions are divided into control layer and data layer. In the SDN, the global view is achieved to receive information by centralization, and then the deployment is dynamically adjusted to control the network. In this paper, we summarize the advantage structure and problems of a newly developed system SDCDN (software defined content distributed network). We introduce similar research progress. Then we summarize the current situation of SDN and CDN, compare and analyze their advantages and disadvantages, and finally put forward the directions of open issues and the future research.

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Section: Introductionmentioning

confidence: 99%

A Review on Software Defined Content Delivery Network: A Novel Combination of CDN and SDN

Yang

Pan

2023

IEEE Access

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“…The emergence of adaptive streaming marked a pivotal shift, allowing the seamless adjustment of video quality in response to changing network parameters. This evolution has been accelerated by the proliferation of high-speed internet, the rise of content delivery networks (CDNs) [22], [21], [3], and the widespread adoption of streaming platforms. Adaptive streaming protocols, such as HTTP Live Streaming (HLS) [1], [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] and Dynamic Adaptive Streaming over HTTP (DASH) [20], [5], [18], have become industry standards, shaping the contemporary landscape of video streaming.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers

Khan

2023

International Transactions on Electrical Engineering and Comput

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This review paper explores the paradigm of personalized adaptive streaming, where machine learning techniques are employed to tailor video streaming experiences based on individual user behavior, preferences, and contextual factors. The paper begins by elucidating the evolution of video streaming and the critical role of adaptive streaming in modern multimedia consumption. It provides a comprehensive overview of adaptive video streaming, covering its basics, traditional approaches, and associated challenges. Emphasizing the significance of personalization in enhancing user experience, the paper then delves into the integration of machine learning in adaptive streaming systems. Specific personalized adaptive streaming techniques, including user profiling, context-aware adaptation, and real-time adjustments based on user behavior, are discussed in detail. Case studies and applications showcase notable platforms, successes, and challenges. A comparative analysis of machine learning models and algorithms is conducted, followed by a discussion on current challenges, ethical considerations and future research directions. The paper concludes by summarizing key findings and urging researchers and industry practitioners to contribute to the evolving landscape of personalized adaptive streaming.

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“…In this sense, content delivery networks (CDNs) have been proposed to handle the delivery of data between applications distributed through multiple environments (any of the edge, the fog, or the cloud) [25,26]. Commonly, CDNs follow a centralized approach where the contents produced by a sensor or applications are stored in large storage servers (usually in the cloud or the fog) and then distributed to the applications/end-users that require the data for processing or visualization [27,28].…”

Section: Introductionmentioning

confidence: 99%

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

Barron

Sánchez‐Gallegos

Carrizales-Espinoza

et al. 2022

Sensors

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Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

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Recent Developments in Content Delivery Network: A Survey

Cited by 8 publications

References 21 publications

A Review on Software Defined Content Delivery Network: A Novel Combination of CDN and SDN

A Review on Software Defined Content Delivery Network: A Novel Combination of CDN and SDN

Adaptive Video Streaming: Navigating Challenges, Embracing Personalization, and Charting Future Frontiers

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

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