Prediction-Based Dynamic Resource Allocation for Video Transcoding in Cloud Computing

Jokhio, Fareed; Ashraf, Adnan; Lafond, Sébastien; Porres, Iván; Lilius, Johan

doi:10.1109/pdp.2013.44

Cited by 83 publications

(58 citation statements)

References 23 publications

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“…Most of these works [7,9,[11][12][13] take advantage of the fact that some modern video compression techniques divide the video stream into non-overlapping Group of Pictures (GOPs) that can be treated independently of each other. The encoding time of each GOP depends on its duration and on the complexity of the corresponding scene.…”

Section: Related Workmentioning

confidence: 99%

“…Some authors [7,11] propose fairly complicated systems to estimate the encoding time of each GOP based on real-time measurements, while others [9,12,13] assume that this information is directly available, for instance [9] by profiling the encoding of a few representative videos of different types, similarly to what we have done (see section 3.2). Another downside of a GOP-based solution is that the encoding of each GOP can be completed out of order and then need to be reordered before being delivered to the users.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, some papers [7,9,[11][12][13]] present algorithms to schedule transcoding jobs on a group of computers, typically in order to maximize CPU utilization [7,11] or to minimize the finishing time [12,13]. Other researchers have analyzed the performance of video encoding and the relationship between power, rate and distortion [8,21,25] using analytical models and empirical studies.…”

Section: Introductionmentioning

confidence: 99%

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Transcoding live adaptive video streams at a massive scale in the cloud

Aparicio-Pardo

Pires

Blanc

et al. 2015

Proceedings of the 6th ACM Multimedia Systems Conference

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More and more users are watching online videos produced by non-professional sources (e.g., gamers, teachers of online courses, witnesses of public events) by using an increasingly diverse set of devices to access the videos (e.g., smartphones, tablets, HDTV). Live streaming service providers can combine adaptive streaming technologies and cloud computing to satisfy this demand. In this paper, we study the problem of preparing live video streams for delivery using cloud computing infrastructure, e.g., how many representations to use and the corresponding parameters (resolution and bit-rate). We present an integer linear program (ILP) to maximize the average user quality of experience (QoE) and a heuristic algorithm that can scale to large number of videos and users.We also introduce two new datasets: one characterizing a popular live streaming provider (Twitch) and another characterizing the computing resources needed to transcode a video. They are used to set up realistic test scenarios. We compare the performance of the optimal ILP solution with current industry standards, showing that the latter are sub-optimal. The solution of the ILP also shows the importance of the type of video on the optimal streaming preparation. By taking advantage of this, the proposed heuristic can efficiently satisfy a time varying demand with an almost constant amount of computing resources.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transcoding live adaptive video streams at a massive scale in the cloud

Aparicio-Pardo

Pires

Blanc

et al. 2015

Proceedings of the 6th ACM Multimedia Systems Conference

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show abstract

“…[17] and [18] are control theoretic approaches while [19] and [20] are based on perfomance dynamics. The authors in [21] and [22] use workload prediction. There are two major differences between these works and the work in this paper.…”

Section: Related Workmentioning

confidence: 99%

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Mijumbi

Gorricho

Serrat

et al. 2014

2014 IEEE Network Operations and Management Symposium (NOMS)

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Abstract-Network virtualisation is considerably gaining attention as a solution to ossification of the Internet. However, the success of network virtualisation will depend in part on how efficiently the virtual networks utilise substrate network resources. In this paper, we propose a machine learning-based approach to virtual network resource management. We propose to model the substrate network as a decentralised system and introduce a learning algorithm in each substrate node and substrate link, providing self-organization capabilities. We propose a multiagent learning algorithm that carries out the substrate network resource management in a coordinated and decentralised way. The task of these agents is to use evaluative feedback to learn an optimal policy so as to dynamically allocate network resources to virtual nodes and links. The agents ensure that while the virtual networks have the resources they need at any given time, only the required resources are reserved for this purpose. Simulations show that our dynamic approach significantly improves the virtual network acceptance ratio and the maximum number of accepted virtual network requests at any time while ensuring that virtual network quality of service requirements such as packet drop rate and virtual link delay are not affected.

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“…There are many ways to provide resource management controls proposed by various researchers, we categorize them as: 1) resource management Middleware techniques; 2) Prediction-based resource allocation techniques [8] [9]; 3) Web services; and 4) Virtualization Technology. However, our study proposes a selfadaptive and prediction-based resource allocation mainly because they allow for resource allocation at an application level.…”

Section: Introductionmentioning

confidence: 99%

Application Resource Management for Highly Computational Applications in the Operational Environment: A Critical Review

Balikuddembe¹,

Gudu²

2017

JSEA

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Computational resources have such a significant influence on the operation of any software application, it is therefore important to understand how these applications utilize these resources. Modern resource-intensive enterprise and scientific applications are creating a growing demand for high performance computing infrastructures. They constantly interact with and rely heavily on complex resources. However, they often operate in resource-limited environments yet they often handle massive data, both in size and complexity. Software application services, processes or transactions compete for the much required but scarce resources. This creates the need to improve the existing resource allocation and management issue in such operational environments, as well as propose new ones, if necessary. Software developers try to analyze application operation environment using diverse analysis and design methods. Our aim therefore, is to design a tool that is able to work with a hybrid of adaptive and prediction-based resource management and allocation models while applying the priority based job scheduling algorithm to try and solve the application resource management challenges currently being faced in such environments, even if, partially.

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Prediction-Based Dynamic Resource Allocation for Video Transcoding in Cloud Computing

Cited by 83 publications

References 23 publications

Transcoding live adaptive video streams at a massive scale in the cloud

Transcoding live adaptive video streams at a massive scale in the cloud

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Application Resource Management for Highly Computational Applications in the Operational Environment: A Critical Review

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