CVSS: A Cost-Efficient and QoS-Aware Video Streaming Using Cloud Services

Li, Xiangbo; Salehi, Mohsen Amini; Bayoumi, Magdy; Buyya, Rajkumar

doi:10.1109/ccgrid.2016.49

Cited by 38 publications

(43 citation statements)

References 17 publications

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“…Li et al, [6] developed an architecture for CVSS, Cloudbased Video Streaming Service. CVSS utilizes cloud resources to deliver video streams through a balanced combination of ondemand processing and partial caching in order to minimize use of resources and maintain high QoE to viewers.…”

Section: Related Workmentioning

confidence: 99%

“…The problem is that the large and fast-growing repository size of streaming providers has made it infeasible to cache a large portion of the overall content on their CDNs. In addition, caching on CDNs is less effective because of the fact that streaming providers have to maintain multiple versions of the same video to be able to support heterogeneous display devices and network conditions [6]. As such, instead of preprocessing video streams into multiple versions, mechanisms for on-demand processing (e.g., on-demand transcoding [6]) of video streams is becoming prevalent [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, caching on CDNs is less effective because of the fact that streaming providers have to maintain multiple versions of the same video to be able to support heterogeneous display devices and network conditions [6]. As such, instead of preprocessing video streams into multiple versions, mechanisms for on-demand processing (e.g., on-demand transcoding [6]) of video streams is becoming prevalent [7], [8]. However, the challenge is that on-demand video processing cannot be performed on CDNs since they are predominantly used for caching purposes [9].…”

Section: Introductionmentioning

confidence: 99%

“…A large body of research studies have been undertaken to maintain the desired video streaming QoE (e.g., [6], [7]) through efficient use of cloud services. Particularly, the earlier studies have shown that the access pattern of video streams follows a long-tail distribution [11], [14].…”

Section: Introductionmentioning

confidence: 99%

“…Considering this long-tail access pattern, streaming service providers commonly pre-process (store) hot videos or GOPs in multiple versions to fit heterogeneous viewers' devices [11]. Alternatively, they only keep a minimal number of versions for the rarely accessed videos [6], [17]. For any portions of the video that are not pre-processed, they are processed in an on-demand manner upon viewer's request [7].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

F-FDN: Federation of Fog Computing Systems for Low Latency Video Streaming

Veillon

Denninnart

Salehi

2019

2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)

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Video streaming is growing in popularity and has become the most bandwidth-consuming Internet service. As such, robust streaming in terms of low latency and uninterrupted streaming experience, particularly for viewers in distant areas, has become a challenge. The common practice to reduce latency is to pre-process multiple versions of each video and use Content Delivery Networks (CDN) to cache videos that are popular in a geographical area. However, with the fast-growing video repository sizes, caching video contents in multiple versions on each CDN is becoming inefficient. Accordingly, in this paper, we propose the architecture for Fog Delivery Networks (FDN) and provide methods to federate them (called F-FDN) to reduce video streaming latency. In addition to caching, FDNs have the ability to process videos in an on-demand manner. F-FDN leverages cached contents on the neighboring FDNs to further reduce latency. In particular, F-FDN is equipped with methods that aim at reducing latency through probabilistically evaluating the cost benefit of fetching video segments either from neighboring FDNs or by processing them. Experimental results against alternative streaming methods show that both on-demand processing and leveraging cached video segments on neighboring FDNs can remarkably reduce streaming latency (on average 52%).

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

F-FDN: Federation of Fog Computing Systems for Low Latency Video Streaming

Veillon

Denninnart

Salehi

2019

2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC)

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SMSE: A serverless platform for multimedia cloud systems

Denninnart,

Amini Salehi

2023

Concurrency and Computation

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SummaryAlong with the rise of domain‐specific computing (ASICs hardware) and domain‐specific programming languages, we envision that the next step is the emergence of domain‐specific cloud platforms. Considering multimedia streaming as one of the most trendy applications in the IT industry, the goal of this study is to develop serverless multimedia streaming engine (SMSE), the first domain‐specific serverless platform for multimedia streaming. SMSE democratizes multimedia service development via enabling content providers (or even end‐users) to rapidly develop their desired functionalities on their multimedia contents. Upon developing SMSE, the next goal of this study is to deal with its efficiency challenges and develop a function container provisioning method that can efficiently utilize cloud resources and improve the users' quality of service. In particular, we develop a dynamic method that provisions durable or ephemeral containers depending on the spatiotemporal and data‐dependency characteristics of the functions. Evaluating the prototype implementation of SMSE under real‐world settings demonstrates its capability to reduce both the containerization overhead, and the makespan time of serving multimedia processing functions (by up to 30%) in compare to the function provision methods that are being used in the general‐purpose serverless cloud systems.

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Leveraging Computational Reuse for Cost- and QoS-Efficient Task Scheduling in Clouds

Denninnart

Salehi

Toosi

et al. 2018

Service-Oriented Computing

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Cloud-based computing systems could get oversubscribed due to budget constraints of cloud users which causes violation of Quality of Experience (QoE) metrics such as tasks' deadlines. We investigate an approach to achieve robustness against uncertain task arrival and oversubscription through smart reuse of computation while similar tasks are waiting for execution. Our motivation in this study is a cloud-based video streaming engine that processes video streaming tasks in an on-demand manner. We propose a mechanism to identify various types of "mergeable" tasks and determine when it is appropriate to aggregate tasks without affecting QoS of other tasks. Experiment shows that our mechanism can improve robustness of the system and also saves the overall time of using cloud services by more than 14%.

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CVSS: A Cost-Efficient and QoS-Aware Video Streaming Using Cloud Services

Cited by 38 publications

References 17 publications

F-FDN: Federation of Fog Computing Systems for Low Latency Video Streaming

F-FDN: Federation of Fog Computing Systems for Low Latency Video Streaming

SMSE: A serverless platform for multimedia cloud systems

Leveraging Computational Reuse for Cost- and QoS-Efficient Task Scheduling in Clouds

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