Performance Of Content Delivery Networks Research Articles

Analysis performance of content delivery network by used Rateless Code method

Content Delivery Network (CDN) is a server system network to content contribution in an application/web to various accessors around the world so that data sent and received by users was faster. Server selection algorithm determines the extent to which the quality of server service to client requests. This study to analyze CDN performance, used Geographical algorithm Domain Name Server (GeoDNS), Round Robin (RR), Weigthed Round Robin (WRR), dan Least Connection (LC). In addition, Rateless Code (RLC) is also applied to reduce packet loss, there are two methods used, namely the first method is rateless code 50 and the second method is rateless code 100. Thus, the trial was carried out 20 times. Besides that, it is also applied Rateless Code (RLC) to less packet loss. From the research results, the use of CDN can improve server service performance. CDN managed to reduce the maximum to the average delay about 67,6%. From the studied CDN algorithm, Geo DNS experienced the lowest delay up to about 67.6 ms, however LC experienced the lowest packet loss, namely about 2,84%. The application of rateless code on CDN with LC algorithm succeeded in reducing packet loss to about 2.84%, but delay increased to about 75.3%.

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.

A Practical Learning-based Approach for Viewer Scheduling in the Crowdsourced Live Streaming

Scheduling viewers effectively among different Content Delivery Network (CDN) providers is challenging owing to the extreme diversity in the crowdsourced live streaming (CLS) scenarios. Abundant algorithms have been proposed in recent years, which, however, suffer from a critical limitation: Due to their inaccurate feature engineering or naive rules, they cannot optimally schedule viewers. To address this concern, we put forward LTS (Learn to Schedule), a novel scheduling algorithm that can adapt to the dynamics from both viewer traffics and CDN performance. In detail, we first propose LTS-RL, an approach that schedules CLS viewers based on deep reinforcement learning (DRL). Since LTS-RL is trained in an end-to-end way, it can automatically learn scheduling algorithms without any pre-programmed models or assumptions about the environment dynamics. At the same time, to practically deploy LTS-RL, we then use the decision tree and imitation learning to convert LTS-RL into a more light-weighted and interpretable model, which is denoted as Fast-LTS. After the extensive evaluation of the real data from a leading CLS platform in China, we demonstrate that our proposed model (both LTS-RL and Fast-LTS) can improve the average quality of experience (QoE) over state-of-the-art approaches by 8.71--15.63%. At the same time, we also demonstrate that Fast-LTS can faithfully convert the complicated LTS-RL with slight performance degradation (< 2%), while significantly reducing the decision time (×7--10).

A general AI-defined attention network for predicting CDN performance

Abstract It has become an increasingly popular trend to replicate network content across a geographically distributed structure. The Content Delivery Network (CDN) is a prime example. Content providers have been struggling to improve the Quality of Experience (QoE) of its clients via accurate predictions of CDN performance, which is a crucial prerequisite for subsequent policies like resource scheduling. Methods of Artificial Intelligence (AI) like machine learning and deep learning have shown their advantages on performance predictions recently, but it is still challenging to simultaneously ensure accuracy, generality and low overhead. In this paper, we propose a general AI-defined attention network to make predictions on CDN performance, the results of which can improve CDN resource optimal scheduling. First, we formulate this CDN performance prediction problem as a sequence learning problem. And then we design a two-phase deep neural network with attention so that our model can work well for all cache groups in a CDN. Experiment results show that our model outperforms state-of-art models in accuracy while maintaining a relatively low training overhead.

Peer‐assisted content delivery network by vehicular clouds: Algorithm and evaluation

In this paper, we study how to improve the performance of content delivery networks (CDNs) using the novel concept of vehicular clouds. Our main idea is to use a cluster of connected vehicles (ie, vehicular clouds) to act as virtual servers in CDNs in order to relieve the workload of physical CDN servers. The key benefits include guaranteed content availability/reliability in terms of mean time between failures and optimized data storage workload at vehicular clouds. Workload is an important metric as it is directly related to the latency of content delivery. Specifically, we present a content placement algorithm that has an optimal workload at vehicular clouds. Finally, we verify the system efficacy using our own simulator based on simulation of urban mobility (SUMO) and Python.

Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks.

Coded multicasting has been shown to be a promising approach to significantly improve the performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-optimal performance require content items to be partitioned into several packets that grows exponentially with the number of caches, leading to codes of exponential complexity that jeopardize their promising performance benefits. In this paper, we address this crucial performance-complexity tradeoff in a heterogeneous caching network setting, where edge caches with possibly different storage capacity collect multiple content requests that may follow distinct demand distributions. We extend the asymptotic (in the number of packets per file) analysis of shared link caching networks to heterogeneous network settings, and present novel coded multicast schemes, based on local graph coloring, that exhibit polynomial-time complexity in all the system parameters, while preserving the asymptotically proven multiplicative caching gain even for finite file packetization. We further demonstrate that the packetization order (the number of packets each file is split into) can be traded-off with the number of requests collected by each cache, while preserving the same multiplicative caching gain. Simulation results confirm the superiority of the proposed schemes and illustrate the interesting request aggregation vs. packetization order tradeoff within several practical settings. Our results provide a compelling step towards the practical achievability of the promising multiplicative caching gain in next generation access networks.

PCR: caching replacement algorithm in proxy server

The efficiency of caching is a key factor affecting the performance of content delivery network (CDN). The main aim of current CDN caching is to obtain a higher hit ratio, since the validation and freshness of outdated pages have not received due consideration in their replacement model. In this paper, a new improved cache profit model is clearly defined, and the freshness factors of web pages have been taken into adequate account. Based on the profit model, a new replacement algorithm – proxy cache replacement (PCR) – is recommended, and it can be proved optimal under the rational hypothesis. To conclude, a series of comparative experiments verified the efficiency of PCR in web caching replacement.

Toy-IoT-Oriented data-driven CDN performance evaluation model with deep learning

Abstract Content Delivery Network(CDN) is geographically distributed network of cache servers. It can deliver the Internet content based on the users’ geographic position and real-time quality of service(QoS). Now with the rapid development of the Internet of things, IoT also needs CDN acceleration. Therefore, CDN not only needs to serve people, but also needs to serve IoT, such as sensors,toys. Especially for toy computing, CDN need to sink further to support the uplink data transmission. Because the performance of CDN is crucial to the resource management of existing platform and the acceleration of data transmission, CDN providers use different models to exactly describe the performance of CDN. Traditional models are linear models or need to be adjusted manually. These normally existing drawbacks make the methods hardly to be applied stably. Recently, deep learning(DL) has made great breakthroughs in solving many problems. We use the RNN in deep learning to model the CDN performance. Our design can exactly capture the nonlinear relationship between high-dimensional machine data and the CDN performance. And it can also realize the correct prediction of the reach rate which is the CDN’ main performance evaluation index in our design.The experimental results have shown that our model is able to outperform state-of-the-arts models.

Improving Multimedia Content Delivery via Augmentation With Social Information: The Social Prefetcher Approach

A significantly large proportion of HTTP traffic results from bandwidth-intensive multimedia content circulating through online social networks (OSNs). With multimedia content providers, such as YouTube, often relying on content delivery network (CDN) infrastructures, the pursuit lies in exploiting the user activity extracted from OSNs to improve the content prefetching mechanism. Aiming to reduce bandwidth usage, we incorporated a dynamic mechanism to CDNsim, a stand-alone CDN traffic simulator. This mechanism is based on a dynamic policy that takes patterns of information transmission over OSNs into account. Herein, we demonstrate that the performance of CDNs can be improved, and the cost of copying to surrogate servers is taken into consideration.

SAViNE: social network analysis - inspired content delivery network deployment and experimentation

Abstract Over the last years, the content delivery network (CDN) market has been witnessing globally an increase in the development of cloud-based CDNs, as they constitute a viable and cost-effective alternative to traditional commercial CDNs. Towards that direction, Social-Aware Virtual Network Embedding (SAViNE) for wireless content delivery within the OpenLab project aims at establishing, assessing, and prototyping a novel framework for deploying a CDN over the wireless cloud. Specifically, the goal of SAViNE is to provide experimental validation of the efficiency of social network analysis (SNA) - inspired surrogate server placement strategies, devised for fostering content delivery. The proposed framework has been integrated with the OpenLab experimental facilities, providing a run-time environment for CDN deployment, operation, and performance evaluation over the wireless environment. The framework’s feasibility and scalability is experimentally validated. The study is complemented with repeatability evaluation of experimental measurements, related to the resulting CDN’s operational efficiency.

Modeling of Router-based Request Redirection for Content Distribution Network

The increase of digital data between content’s servers and clients in a network causes congestion problems when downloading big web contents including files, streaming media, etc. The problem becomes pronounced when a narrow bandwidth or unexpected termination of streaming services is appeared. Content Delivery Network (CDN) is introduced to overcome such problems by redirect client’s request to the best server which proximal and least loaded. However, with intensity increase of people accessing a particular content, the performance of CDN is reduced and sometimes congested. We propose an architecture to solve such problems with a new method to redirect client’s request through a special router called Service-oriented Router (SoR). In this paper, several analytical studies and experiments have been conducted and the result show that router-based redirection is more effective than DNS-based redirection that is currently being used. SoR has been introduces as a content-based router and has capability to do deep packet inspection (DPI) into the packet streams and analyze them. We use SoR in our proposed method as a CDN’s core-router to redirect client’s request to the best server. Our experiment conducted with model and numerical analytic supported by optimal redirection probability based on delay between router and server. The experiment result confirmed that the router-based redirection reduced the response time by 23.3% compared to the DNSbased redirection.

Analysing the Media Streaming for QoS in Content Delivery Networks

prediction of performance in CDN (Content Delivery Network) used to improve the technology development. The proposed analysis used how to design a network according to the applications of CDN network experiments with different workloads. This paper shows the variations of any parameter must affect the performance of content delivery network. The Average end-to-end delay, for Quality of service requirements, which are evaluated in this paper and event analysis also, be performed.