A version-aware computation and storage trade-off strategy for multi-version VoD systems in the cloud

Zhao, Hui; Zhang, Weizhan; Du, Biao; Chen, Yuxuan

doi:10.1109/iscc.2015.7405635

Cited by 18 publications

(15 citation statements)

References 17 publications

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“…They determine how long a video should be stored or how frequently it should be retranscoded from a given source video. Zhao et al [32] take the popularity, computation cost, and storage cost of each version of a video stream into account to determine versions of a video stream that should be stored or transcoded. The earlier studies demonstrate that it is possible to transcode infrequently accessed videos streams in an on-demand manner [33].…”

Section: Related Workmentioning

confidence: 99%

Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

Salehi

Bayoumi

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Video streams, either in the form of Video On-Demand (VOD) or live streaming, usually have to be converted (i.e., transcoded) to match the characteristics of viewers' devices (e.g., in terms of spatial resolution or supported formats). Transcoding is a computationally expensive and time-consuming operation. Therefore, streaming service providers have to store numerous transcoded versions of a given video to serve various display devices. With the sharp increase in video streaming, however, this approach is becoming cost-prohibitive. Given the fact that viewers' access pattern to video streams follows a long tail distribution, for the video streams with low access rate, we propose to transcode them in an on-demand (i.e., lazy) manner using cloud computing services. The challenge in utilizing cloud services for on-demand video transcoding, however, is to maintain a robust QoS for viewers and cost-efficiency for streaming service providers. To address this challenge, in this paper, we present the Cloud-based Video Streaming Services (CVS2) architecture. It includes a QoS-aware scheduling component that maps transcoding tasks to the Virtual Machines (VMs) by considering the affinity of the transcoding tasks with the allocated heterogeneous VMs. To maintain robustness in the presence of varying streaming requests, the architecture includes a cost-efficient VM Provisioner component. The component provides a selfconfigurable cluster of heterogeneous VMs. The cluster is reconfigured dynamically to maintain the maximum affinity with the arriving workload. Simulation results obtained under diverse workload conditions demonstrate that CVS2 architecture can maintain a robust QoS for viewers while reducing the incurred cost of the streaming service provider by up to 85%.

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Section: Related Workmentioning

confidence: 99%

Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

Salehi

Bayoumi

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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“…Media streaming applications are often deployed in the cloud in order to avail the benefit of IaaS such as scalability, flexibility, etc. Management of streaming applications (deployed using cloud IaaS) with respect to maintaining the QoS for their viewers has recently received attention from the cloud researchers [28], [29], [30], [31]. Most recently the work in [13] have proposed an on-demand video stream transcoding for streaming applications deployed in the cloud in order to maximise viewers' satisfaction.…”

Section: B Managing Media Streaming Applications In the Cloudmentioning

confidence: 99%

Supporting Cloud IaaS Users in Detecting Performance-Based Violation for Streaming Applications

Barlaskar

Dichev

Kilpatrick

et al. 2018

2018 IEEE International Conference on Autonomic Computing (ICAC)

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Cloud infrastructure-as-a-service (IaaS) provides on-demand resources at low cost. IaaS users anticipate stable performance for their applications so that the applications' Quality of Service (QoS) constraints are satisfied. However, they generally experience performance variability in their deployed applications, primarily due to multi-tenancy. Such variability may lead to QoS violations for the applications. Existing cloud infrastructure solutions offered by different cloud providers (CPs) do not have facilities to detect such QoS violations. Hence, cloud users need to take responsibility for monitoring the performance of their applications in order to detect application-specific QoS violations. To support cloud users on this, researchers have proposed a few user-centric cloud monitoring frameworks. However, these frameworks do not detect application-specific QoS violations. In this paper, we propose a novel algorithm for detecting QoS violation for media streaming applications. At each detection instance, the algorithm compares the cumulative value of the expected streamed data against the cumulative value of the measured streamed data. Based on this comparison, the algorithm may raise QoS violation alarms. We evaluate the algorithm by deploying a media streaming application in a lab-based cloud set-up. Experimental results demonstrate that the proposed algorithm can reliably detect QoS violation for streaming applications deployed in the cloud.

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“…Zhao et al [12] developed a method that reduces the operational cost of video streaming on the cloud. Particularly, their approach trades off between the transcoding and storing the video, they implemented it by using the weight graph of the video transcoding.…”

Section: Related Workmentioning

confidence: 99%

“…They demonstrated that their method reduces 30% of operational cost by compared to storing the whole video. Zhao et al [14] proposed an approach to trade-off between computation and storage costs and to minimize the cost for multi version videos. They utilized the transcoding weight graph which is the transcoding relationships among versions of a video, along with the popularity of those different versions of the video.…”

Section: Related Workmentioning

confidence: 99%

Cost Efficient Repository Management for Cloud-Based On-demand Video Streaming

Darwich¹,

Beyazıt²,

Salehiy³

et al. 2017

2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud)

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Video stream is converted to several formats to support the user's device, this conversion process is called video transcoding, which imposes high storage and powerful resources. With emerging of cloud technology, video stream companies adopted to process video on the cloud. Generally, many formats of the same video are made (pre-transcoded) and streamed to the adequate user's device. However, pre-transcoding demands huge storage space and incurs a high-cost to the video stream companies. More importantly, the pre-transcoding of video streams could be hierarchy carried out through different storage types in the cloud. To minimize the storage cost, in this paper, we propose a method to store video streams in the hierarchical storage of the cloud. Particularly, we develop a method to decide which video stream should be pre-transcoded in its suitable cloud storage to minimize the overall cost. Experimental simulation and results show the effectiveness of our approach, specifically, when the percentage of frequently accessed videos is high in repositories, the proposed approach minimizes the overall cost by up to 40%.

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A version-aware computation and storage trade-off strategy for multi-version VoD systems in the cloud

Cited by 18 publications

References 17 publications

Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

Supporting Cloud IaaS Users in Detecting Performance-Based Violation for Streaming Applications

Cost Efficient Repository Management for Cloud-Based On-demand Video Streaming

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