Scheduling a Video Transcoding Server to Save Energy

Song, Minseok; Lee, Yeongju

doi:10.1145/2700282

Cited by 19 publications

(13 citation statements)

References 40 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many other works focus instead on the optimal choice of the encoding bitrates and online transcoding operations [90,97]. In online transcoding, only few video representations are prepared by the server before the content is published; all the others are prepared on-the-fly when and if the clients request them.…”

Section: Stream Prioritizationmentioning

confidence: 99%

“…Moreover, online transcoding is very computational intensive. Song et al develop a scheduling algorithm for power-efficient transcoding tasks [90]. An optimization problem is formulated to assign each transcoding job to the right CPU, whose frequency is adjusted to save energy while meeting the transcoding deadline.…”

Section: Stream Prioritizationmentioning

confidence: 99%

“…Transcoding operations allow to reduce the storage requirements in CDN nodes, as the content is prepared on-the-fly if requested, but increase the risk of video freezes. Relevant references: [4], [20], [97], [90], [45], [49], [103].…”

Section: Stream Prioritizationmentioning

confidence: 99%

“…Complexity References Rerouting ++ + M/H [28], [6], [13], [68], [10], [101] Bandwidth shaping ++ +/-++ M [19], [36], [46], [7], [63], [65], [73] Cross-layer optimization ++ ++ M/H [14], [109], [29], [33], [26] Prioritization ++ M [75], [76], [110] CDN and caching ++ -+ M [12], [9], [67], [62], [48], [18], [54], [1], [35], [55], [58] Server-assistance + (-) E/M [4], [20], [97], [90], [45], [49], [103] Sec [60], [78], [80], [38], [56], [108] functionalities in ICN [38]. In this work, a video client can opportunistically retrieve video segments from both the server, using 3G/4G, and from other clients in a peer-to-peer fashion, using Wi-Fi.…”

Section: Quality Switches Freezes Fairness Latencymentioning

confidence: 99%

See 3 more Smart Citations

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Petrangeli

Hooft

Wauters

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this paper, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.

show abstract

Section: Stream Prioritizationmentioning

confidence: 99%

Section: Stream Prioritizationmentioning

confidence: 99%

Section: Stream Prioritizationmentioning

confidence: 99%

Section: Quality Switches Freezes Fairness Latencymentioning

confidence: 99%

See 2 more Smart Citations

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Petrangeli

Hooft

Wauters

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, they did not consider the scheduling problems for performing a task on multiple VMs in parallel. [68,71,93] studied the problem of scheduling tasks under a fixed amount of resources. However, these methods cannot achieve resource efficiency and desired performance requirements without scaling computing capacities under time-varying workloads.…”

Section: Resource Provisioning For Video Transcodingmentioning

confidence: 99%

Towards resource-efficient and QoS-aware video adaptation in media cloud

Gao¹

View full text Add to dashboard Cite

Video streaming dominates Internet traffic, accounting for more than 70 percent of North American downstream traffic at peak time. However, limited bandwidth capacity, unstable network condition, and diverse viewing devices inherently deteriorate user experiences, triggering a tussle between the growing demand of video traffic and the quality of viewing experiences. Video adaptation is the de facto solution for video streaming over heterogeneous viewing devices and under time-varying network connections. For video adaptation, each video must be transcoded into multiple representations in different bitrates and resolutions. The client-side can dynamically select the best possible quality representation according to the current network condition and device capacity. Nevertheless, embracing video adaptation in video streaming faces many challenges regarding operational cost, Quality of Service (QoS), and Quality of Experience (QoE). First, video transcoding is compute-intensive, and transcoding source videos into multiple representations and storing them consume tremendous resources. Adopting video adaptation mechanism can thus greatly increase the operational cost for video streaming. To reduce the operational cost, we propose the partial transcoding scheme for cost-efficient video transcoding. Specifically, the frequently requested video chunks are cached, resulting in storage cost; while the seldom requested video chunks are transcoded online when being requested, resulting in computing cost. We aim to minimize the longterm overall cost by determining whether a video chunk should be cached or transcoded online. We also design the virtual caching scheme, vCache, by considering the practical implementation under the Network Functions Virtualization (NFV) infrastructure. v-Cache can dynamically provision computing resources to ensure that transcoding delays will not affect streaming services. Second, the video generation rate in an online video service is time-varying, and maintaining a fixed number of servers for transcoding to meet the peak workload may waste i I must deeply thank my supervisor, Dr. Yonggang Wen, who leaded me into the wonderful land of scientific research. The journey of learning to be a qualified researcher is never easy, with Dr. Wen's patient and insightful guidance, I avoided numerous mistakes as a junior Ph.D. student. This thesis cannot be completed in time without Dr. Wen's help. During the past four years as Dr. Wen's Ph.D. student, I am impressed by the glorious personality of Dr. Wen, his enthusiasm on work, discipline on management, patience on improving others, and inspiration on innovation, etc. He always encourages us to think logically on research problems, clearly present ideas, and rigorously formulate mathematical models, which are always in my mind and invaluable to my future career.

show abstract

Video Quality Adaptation for Limiting Transcoding Energy Consumption in Video Servers

Lee

Lee²,

Song

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Dynamic adaptive streaming over HTTP (DASH) inherently requires many transcoding operations to produce various bit-rate versions. However, transcoding is highly computationally intensive, resulting in significant power consumption in transcoding servers. To limit the amount of energy consumption in transcoding servers, some transcoding tasks can be skipped and executed later, but this poses several questions, such as (1) which versions can be transcoded before an actual streaming session, (2) how to limit total amount of transcoding energy consumption, and (3) how to handle requests to the untranscoded versions. To address this, we first introduce the concept of transcoding gain to express the overall video quality assigned to each transcoded version by considering segment popularity and go on to propose an algorithm that determines when and which versions should be transcoded with the aim of maximizing the overall transcoding gain by limiting energy consumption. To achieve this, it allows lower bit-rate versions to be streamed than are requested, at the cost of minimal video quality degradation by considering the overall video quality. The proposed scheme was implemented on a clustered system where various issues were resolved. The experimental results show that the transcoding selection algorithms can effectively control the amount of power consumption, minimizing the effect of overall video quality degradation. For example, it can save up to 65% energy when video quality degradation is allowed to be 4.3% at maximum compared with the scheme that transcodes all the versions.

show abstract

Scheduling a Video Transcoding Server to Save Energy

Cited by 19 publications

References 40 publications

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Quality of Experience-Centric Management of Adaptive Video Streaming Services

Towards resource-efficient and QoS-aware video adaptation in media cloud

Video Quality Adaptation for Limiting Transcoding Energy Consumption in Video Servers

Contact Info

Product

Resources

About