Quality-Aware Transcoding Task Allocation Under Limited Power in Live-Streaming Systems

Lee, Dayoung; Song, Minseok

doi:10.1109/jsyst.2021.3103526

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…[17] proposed a cloud transcoding scheme for both delay-tolerant and delaysensitive videos with different priorities. In [19], a scheme was designed to limit the peak power consumption while maximizing the total processing capacity in a server with heterogeneous processors using dynamic programming. [20] designed recurrent network and convolutional network based approaches to forecast the approximate transcoding resources which is reserved for transcoding and to maximize the quality of service (QoS).…”

Section: A Cloud Transcodingmentioning

confidence: 99%

Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

Liu

Derakhshani

Mihaylova

et al. 2023

IEEE J. Select. Areas Commun.

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Section: A Cloud Transcodingmentioning

confidence: 99%

Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

Liu

Derakhshani

Mihaylova

et al. 2023

IEEE J. Select. Areas Commun.

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Cost-Effective, Quality-Oriented Transcoding of Live-Streamed Video on Edge-Servers

Lee

Kim

Song

2023

IEEE Trans. Serv. Comput.

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Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs

Kim

Song

2022

Energies

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Dynamic adaptive streaming over HTTP (DASH) technique, the most popular streaming method, requires a large number of hard disk drives (HDDs) to store multiple bitrate versions of many videos, consuming significant energy. A solid-state drive (SSD) can be used to cache popular videos, thus reducing HDD energy consumption by allowing I/O requests to be handled by an SSD, but this requires effective HDD power management due to limited SSD bandwidth. We propose a new SSD cache management scheme to minimize the energy consumption of a video storage system with heterogeneous HDDs. We first present a technique that caches files with the aim of saving more HDD energy as a result of I/O processing on an SSD. Based on this, we propose a new HDD power management algorithm with the goal of increasing the number of HDDs operated in low-power mode while reflecting the heterogeneous HDD power characteristics. For this purpose, it assigns a separate parameter value to each I/O task based on the ratio of HDD energy to bandwidth and greedily selects the I/O tasks handled by the SSD within limits on its bandwidth. Simulation results show that our scheme consumes between 12% and 25% less power than alternative schemes under the same HDD configuration.

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Quality-Aware Transcoding Task Allocation Under Limited Power in Live-Streaming Systems

Cited by 3 publications

References 36 publications

Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

Cost-Effective, Quality-Oriented Transcoding of Live-Streamed Video on Edge-Servers

Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs

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