Content delivery networks: Insights and recent advancement

Sarkar, Darothi; Rakesh, Nitin; Mishra, K. K.

doi:10.1109/pdgc.2016.7913113

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…That means to analyze data storing structures-the data representation format (textual or binary form), the data encryption type, etc.-clearly; one of the most important conditions prior to efficient data processing is the proper location for storing. The most frequent solutions today are XML files, distributed file systems [12], content delivery networks [13], clouds, or special databases [14]. The concrete efficient solution depends on the data type.…”

Section: Big Data Reading and Storingmentioning

confidence: 99%

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Dresp-Langley

Ekseth²,

Fesl

et al. 2019

Applied Sciences

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Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

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Section: Big Data Reading and Storingmentioning

confidence: 99%

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Dresp-Langley

Ekseth²,

Fesl

et al. 2019

Applied Sciences

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“…They also discussed several potential avenues for further research in replica server placement in CDNs. A similar comprehensive survey of replica server placement algorithms is also given in [6], which discusses a number of algorithms and compares their performance based on various optimization factors.…”

Section: Related Workmentioning

confidence: 99%

“…Constraint (4) indicates that the origin server caches all of the contents provided in the network and constraint (5) ensures that server v can serve the user requests for content item y only when y is cached in the server. Constraint (6) ensures that a content request can be served by a server at node v only if node v is deployed as a replica server, while constraint (7) ensures that content can be cached at server v only if node v is deployed with a content server. Constraint (8) ensures that the total amount of request load of content item y for user u, as served by different servers, never exceeds the request load of this content item from this user, which is normalized as the content item's UVP of this user.…”

Section: Variables: R Vmentioning

confidence: 99%

Joint Replica Server Placement, Content Caching, and Request Load Assignment in Content Delivery Networks

Bose

et al. 2018

IEEE Access

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With the explosive growth of information and communication technology and its services, some popular Websites currently generate an enormous amount of Internet traffic. A content delivery network (CDN) would then become imperative for supporting such services efficiently. In this paper, we propose joint optimizing approaches for replica server placement, content caching in selected servers, and content request load assignment among the servers, aiming to minimize the ratio of unserved content request load when the network resources and server capacity are both limited. For this, we develop a mixed integer linear programming (MILP) optimization model. To mitigate the computational complexity of the MILP model, we further decompose the optimization problem into three sub-problems, including: 1) choosing the replica server nodes optimally; 2) deciding the content items to be cached in the replica servers; and 3) allocating the content request loads from users onto different servers. For these sub-problems, we develop corresponding heuristic algorithms and show that the proposed approach is not only efficient but also performs very close to the MILP model. We also find that a number of system limitations, such as different numbers of replica servers placed, link capacity, server processing capacity, and server storage capacity, jointly affect the performance of the CDN. Saturation trends are observed on the performance, which indicate that as long as sufficient resources have already been provided, augmenting the resources further may only lead to marginal additional performance improvement.

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“…With the advancement of cloud computing and its associated utility, there is an increased proliferation of resource leasing to construct a content delivery network [1]. Owing to the limited features of the content delivery network, the recent trend of work is majorly towards migrating to the cloud-based content delivery network which is mainly due to magnified traffic over large datacenters.…”

Section: Introductionmentioning

confidence: 99%

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

Jayakumar

Sheelvanthmath

Akki

2022

IJECE

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<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>

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Content delivery networks: Insights and recent advancement

Cited by 5 publications

References 18 publications

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Joint Replica Server Placement, Content Caching, and Request Load Assignment in Content Delivery Networks

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

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