Distributed video transcoding on a heterogeneous computing platform

Chang, Zhi Hao; Jong, Bih Fei; Wong, Wei Jing; Wong, M. L. Dennis

doi:10.1109/apccas.2016.7803998

Cited by 10 publications

(8 citation statements)

References 10 publications

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“…Elasticity support for cloud computing [21], and on-demand QoE aware transcoding [22] in 5G networks have been proposed. Video transcoding tasks can be distributed among multiple nodes with Storm [23]. Morph [24] has been used as part of a distributed system for video transcoding, which is able to predict execution time of transcoding [4].…”

Section: Related Workmentioning

confidence: 99%

Online architecture for predicting live video transcoding resources

2019

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End users stream video increasingly from live broadcasters (via YouTube Live, Twitch etc.). Adaptive live video streaming is realised by transcoding different representations of the original video content. Management of transcoding resources creates costs for the service provider, because transcoding is a CPU-intensive task. Additionally, the content must be transcoded within real time with the transcoding resources in order to provide satisfying Quality of Service. The contribution of this paper is validation of an online architecture for enabling live video transcoding with Docker in a Kubernetes-based cloud environment. Particularly, online cloud resource allocation has been focused on by executing experiments in several configurations. The results indicate that Random Forest regressor provided the best overall performance in terms of precision regarding transcoding speed and CPU consumption on resources, and the amount of realised transcoding tasks. Reinforcement Learning provided lower performance, and required more effort in terms of training.

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Section: Related Workmentioning

confidence: 99%

Online architecture for predicting live video transcoding resources

2019

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“…High-resolution video processing and Content Delivery Networks (CDNs) can greatly benefit from MEC and NFV. References [5,[13][14][15][16][17][18][19][20][21][22] summarize some relevant results in this field. In particular, Reference [13] shows the improvement in QoE perceived by users when CDNs are extended to the edge through the use of NFV and cloud technologies.…”

Section: The Immersive Video Use Casementioning

confidence: 99%

“…The subject of real-time video transcoding in a distributed heterogeneous environment is addressed in Reference [20]. The performance of the proposed system scales as the number of nodes increases, up to the point that the scheduler for video frames distribution becomes a bottleneck.…”

Section: The Immersive Video Use Casementioning

confidence: 99%

“…However, most of the works available concerning MEC focus on traditional video networking functionalities, such as video caching or video streaming [5,[13][14][15][16][17][18][19][20][21][22][23][24]. Conversely, the problem of successfully combining end-user apps and ME apps to provide interactive and immersive video services to consumers on a peer-to-peer basis is a topic that has received little attention from researchers so far.…”

Section: Introductionmentioning

confidence: 99%

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Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case

Cattaneo

Giust²,

Meani

et al. 2018

Computers

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Multi-access Edge Computing (MEC) will be a technology pillar of forthcoming 5G networks. Nonetheless, there is a great interest in also deploying MEC solutions in current 4G infrastructures. MEC enables data processing in proximity to end users. Thus, latency can be minimized, high data rates locally achieved, and real-time information about radio link status or consumer geographical position exploited to develop high-value services. To consolidate network elements and edge applications on the same virtualization infrastructure, network operators aim to combine MEC with Network Function Virtualization (NFV). However, MEC in NFV integration is not fully established yet: in fact, various architectural issues are currently open, even at standardization level. This paper describes a novel MEC in an NFV system which successfully combines, at management level, MEC functional blocks with an NFV Orchestrator, and can neutrally support any “over the top” Mobile Edge application with minimal integration effort. A specific ME app combined with an end-user app for the provision of immersive video services is presented. To provide low latency, CPU-intensive services to end users, the proposed architecture exploits High-Performance Computing resources embedded in the edge infrastructure. Experimental results showing the effectiveness of the proposed architecture are reported and discussed.

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“…This kind of data locality is referred to as temporal data locality. Spatial locality, that is accessing data blocks close to each other, is very important in the secure processor setting [23][24][25]. However, the ORAM literature has overlooked data locality and popularity.…”

Section: Introductionmentioning

confidence: 99%

Locality Aware Path ORAM: Implementation, Experimentation and Analytical Modeling

Al-Saleh

Belghith

2018

Computers

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In this paper, we propose an advanced implementation of Path ORAM to hide the access pattern to outsourced data into the cloud. This implementation takes advantage of eventual data locality and popularity by introducing a small amount of extra storage at the client side. Two replacement strategies are used to manage this extra storage (cache): the Least Recently Used (LRU) and the Least Frequently Used (LFU). Using the same test bed, conducted experiments clearly show the superiority of the advanced implementation compared to the traditional Path ORAM implementation, even for a small cache size and reduced data locality. We then present a mathematical model that provides closed form solutions when data requests follow a Zipf distribution with non-null parameter. This model is showed to have a small and acceptable relative error and is then well validated by the conducted experimental results.

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Distributed video transcoding on a heterogeneous computing platform

Cited by 10 publications

References 10 publications

Online architecture for predicting live video transcoding resources

Online architecture for predicting live video transcoding resources

Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case

Locality Aware Path ORAM: Implementation, Experimentation and Analytical Modeling

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