Building cloud-ready video transcoding system for Content Delivery Networks (CDNs)

Zhuang, Zhenyun; Guo, Chun

doi:10.1109/glocom.2012.6503417

Cited by 12 publications

(13 citation statements)

References 8 publications

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“…A number of attempts have been made on the problem of video transcoding in clouds [19], [20], [21], [22], [23], [24], [25], [2], [26], [27]. Garcia et al [24], [25] and Kim et al [19] proposed to use MapReduce parallel computing tools (e.g., Hadoop) to speed up the video transcoding process.…”

Section: Related Workmentioning

confidence: 99%

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QoS-Aware Resource Allocation for Video Transcoding in Clouds

Wei

Cai

Foh

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-As the "biggest big data", video data streaming in the network contributes the largest portion of global traffic nowadays and in future. Due to heterogeneous mobile devices, networks and user preferences, the demands of transcoding source videos into different versions have been increased significantly. However, video transcoding is a time-consuming task and how to guarantee quality-of-service (QoS) for large video data is very challenging, particularly for those real-time applications which hold strict delay requirement such as live TV. In this paper, we propose a cloud-based online video transcoding system (COVT) aiming to offer economical and QoS guaranteed solution for online large-volume video transcoding. COVT utilizes performance profiling technique to obtain the different performance of transcoding tasks in different infrastructures. Based on the profiles, we model the cloud-based transcoding system as a queue and derive the QoS values of the system based on queuing theory. With the analytically derived relationship between QoS values and the number of CPU cores required for transcoding workloads, COVT is able to solve the optimization problem and obtain the minimum resource reservation for specific QoS constraints. A task scheduling algorithm is further developed to dynamically adjust the resource reservation and schedule the tasks so as to guarantee the QoS in runtime. We implement a prototype system of COVT and experimentally study the performance on real-world workloads. Experimental results show that COVT effectively provisions minimum number of resources for predefined QoS. To validate the effectiveness of our proposed method under large scale video data, we further perform simulation evaluation which again shows that COVT is capable to achieve economical and QoS-aware video transcoding in cloud environment.

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Section: Related Workmentioning

confidence: 99%

“…Similarly, Zhuang et al [22] also designed an architecture for video transcoding in content delivery networks. Ashraf et al [20], [21] studied the admission control problem for video streams to prevent blockage of services and the cost-efficient resource provisioning problem for transcoding tasks in clouds.…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Resource Allocation for Video Transcoding in Clouds

Wei

Cai

Foh

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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“…The elasticity can be defined as how much a computational cloud (or a cloud application) fits workload variations, through provisioning and deprovisioning of resources according to the demand [2]. In this regard, several studies [3], [4] have used the cloud to create Content Delivery Networks (CDNs), known as Cloud-CDNs, in order to provide large media content on the Internet, by taking advantage of elasticity to improve service performance and reduce costs, once there is no need to purchase and maintain the infrastructure.…”

Section: Introductionmentioning

confidence: 99%

An OpenFlow-Based Elastic Solution for Cloud-CDN Video Streaming Service

Rego

Bonfim

Ortiz

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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Media streaming services are responsible for the significant increase of Internet traffic. This fact generates the need for better computing resources management in order to maintain such services always available. Cloud computing offers an elastic infrastructure that provides computing resources on demand in order to maintain such services always available. Moreover, emerging network architectures, such as Software Defined Networks, provide mechanisms that simplify the implementation of advanced network functions. In this context, this paper proposes an elastic approach to manage a video streaming service. The solution includes an OpenFlow-based load balancer, and an elasticity management mechanism that increases or decreases the amount of active streaming servers, considering customer's demand. A prototype was developed using a private cloud infrastructure and experiments were performed to show the viability and effectiveness of our solution, in terms of load balancing and elasticity functionality.

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“…It can provide high-quality and scalable media services, while reducing service response time and network congestion [2]. All these advantages make more and more service providers to adopt CDNs for their content distribution [3].…”

Section: Introductionmentioning

confidence: 99%

Trustworthiness management in sharing CDN infrastructure

Lee

2018

2018 International Conference on Information Networking (ICOIN)

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Abstract-Sharing Content Delivery Network (CDN) technologies such as CDN interconnection and cloud-based CDN have facilitated access to the huge volume of content in a cost-effective way. However, content distribution through shared CDN nodes is vulnerable to a wide variety of uncertainties, including unexpected node failure, intentional node disruption for maintenance or potential discrimination of content based on ownership. Trustworthiness can be considered as a key property to overcome the perception of uncertainty before delivering content through sharing CDN infrastructure and provide reliable broadcasting and telecommunications services. Establishing trustworthiness in sharing CDN infrastructure is a challenging task in the absence of the referenced framework. Therefore, we propose a trustworthiness management framework for sharing CDN infrastructure.

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Building cloud-ready video transcoding system for Content Delivery Networks (CDNs)

Cited by 12 publications

References 8 publications

QoS-Aware Resource Allocation for Video Transcoding in Clouds

QoS-Aware Resource Allocation for Video Transcoding in Clouds

An OpenFlow-Based Elastic Solution for Cloud-CDN Video Streaming Service

Trustworthiness management in sharing CDN infrastructure

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