Video transcoding time prediction for proactive load balancing

Deneke, Tewodors; Haile, Habtegebreil; Lafond, Sébastien; Lilius, Johan

doi:10.1109/icme.2014.6890256

Cited by 15 publications

(11 citation statements)

References 14 publications

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“…The experiment testifies that the performance and incurred cost resulted from deploying the proposed suitability matrix conforms with the discretion of the streaming service provider. 10 Tables (a) to (d), show that as the performance preference p decrease (and cost-preference c increases), the value of ∆ th grows. Accordingly, the maximum Suitability value changes from GPU (performance-oriented VM) in Table 4a to General type (cost-oriented VM) in Table 4d.…”

Section: Performance Evaluationmentioning

confidence: 97%

“…Transcoding time estimation plays an important role in both efficient scheduling and resource provisioning. Deneke et al [54] utilize machine learning methods based on the video characteristics (e.g., resolution, frame rate, and bit rate) to predict the transcoding time. Seo et al [22] focus on the transcoding process details to estimate transcoding time, such as discrete cosine transform (DCT), inverse DCT (iDCT), quantization (Q), inverse Q (iQ), motion estimation/motion compensation (ME/MC), variable length coding (VLC), variable length decoding (VLD).…”

Section: Related Workmentioning

confidence: 99%

“…The videos can be downloaded from: https://goo.gl/TE5iJ5 motion video content types. The length of the videos in the benchmark varies in the range of [10,600] Seconds. The size and frame number of the benchmark videos ranges from 5MB to 313MB, and 240 to 10464, respectively.…”

Section: Performance Analysis Of Transcod-ing Operations On Heterogen...mentioning

confidence: 99%

“…One main challenge is to minimize the incurred cost for using cloud services while maintaining QoS (in terms of uninterrupted streaming experience) for their viewers. To overcome this challenge, several research works have been undertaken in estimating video transcoding time [10], [11], video segmentation models [7], [12], scheduling [10], [13], and resource provisioning methods [7], [9]. However, these studies generally focus on elasticity aspect of cloud VMs.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

Salehi

Joshi

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

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High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (i.e., transcoding) video streams to match the characteristics of viewers' devices (e.g., in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.

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Section: Performance Evaluationmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Performance Analysis Of Transcod-ing Operations On Heterogen...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

Salehi

Joshi

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

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“…To simulate YouTube-like user-provided videos, we choose 200 videos from the dataset in [31]. The durations of all videos are in the range of [120, 180] seconds, and their coding rates fall within [300, 350] kbps.…”

Section: Performance Evaluation a Simulation Setupmentioning

confidence: 99%

Collaborative Content Distribution With an End-to-End Caching Framework

Xing

Song

2020

IEEE Access

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While the mobile networks are evolving to a content-centric paradigm, the surging traffic demands keep stressing the ever-increasing network capacities. In-network caching and particularly edge caching offer an effective means to alleviate the traffic pressure by saving bandwidth resources and balancing traffic loads. In this paper, we consider an integrated content distribution model with universal in-network caching in an end-to-end scope. Based on this model, we formulate two key problems to enable collaborative content distribution across domains, i.e., a request screening problem at the mobile edge and a request routing problem at the integrated edge-core. We propose effective solutions for the two problems, which include a joint device caching and matching algorithm for request screening, and a joint source selection and flow routing algorithm for request routing. The simulation results demonstrate that the proposed solutions achieve significant performance gain over traditional approaches in relieving network congestion by exploiting network dynamics and user contexts. INDEX TERMS Mobile edge computing, content distribution, context-aware, in-network caching, network congestion.

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