Implementation of cloud based live streaming for surveillance

Oza, Neel; Gohil, N. B.

doi:10.1109/iccsp.2016.7754297

Cited by 11 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Servicing Video on Demand and Live TV Content from cloud servers have been studied widely (Lee et al, 2013;Huang et al, 2011;He et al, 2014;Chang et al, 2016;Oza and Gohil, 2016). The reliability of content over the cloud servers have been first considered for video streaming applications in (Al-Abbasi and Aggarwal, 2018d).…”

Section: Notes and Open Problemsmentioning

confidence: 99%

Modeling and Optimization of Latency in Erasure-coded Storage Systems

Aggarwal¹,

Lan²

2020

Preprint

View full text Add to dashboard Cite

As consumers are increasingly engaged in social networking and E-commerce activities, businesses grow to rely on Big Data analytics for intelligence, and traditional IT infrastructures continue to migrate to the cloud and edge, these trends cause distributed data storage demand to rise at an unprecedented speed. Erasure coding has seen itself quickly emerged as a promising technique to reduce storage cost while providing similar reliability as replicated systems, widely adopted by companies like Facebook, Microsoft and Google. However, it also brings new challenges in characterizing and optimizing the access latency when erasure codes are used in distributed storage. The aim of this monograph is to provide a review of recent progress (both theoretical and practical) on systems that employ erasure codes for distributed storage.In this monograph, we will first identify the key challenges and taxonomy of the research problems and then give an overview of different approaches that have been developed to quantify and model latency of erasure-coded storage. This includes recent work leveraging MDS-Reservation, Fork-Join, Probabilistic, and Delayed-Relaunch scheduling policies, as well as their applications to characterize access latency (e.g., mean, tail, asymptotic latency) of erasure-coded distributed storage systems. We will also extend the problem to the case when users are streaming videos from erasure-coded distributed storage systems. Next, we bridge the gap between theory and practice, and discuss lessons learned from prototype implementation. In particular, we will discuss exemplary implementations of erasure-coded storage, illuminate key design degrees of freedom and tradeoffs, and summarize remaining challenges in real-world storage systems such as in content delivery and caching. Open problems for future research are discussed at the end of each chapter.

show abstract

Section: Notes and Open Problemsmentioning

confidence: 99%

Modeling and Optimization of Latency in Erasure-coded Storage Systems

Aggarwal¹,

Lan²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Video Streaming over Cloud: Servicing Video on Demand and Live TV Content from cloud servers have been studied widely [31]- [35]. The placement of content and resource optimization over the cloud servers have been considered.…”

Section: Related Workmentioning

confidence: 99%

Video Streaming in Distributed Erasure-Coded Storage Systems: Stall Duration Analysis

Al-Abbasi

Aggarwal

2018

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

The demand for global video has been burgeoning across industries. With the expansion and improvement of videostreaming services, cloud-based video is evolving into a necessary feature of any successful business for reaching internal and external audiences. This paper considers video streaming over distributed systems where the video segments are encoded using an erasure code for better reliability thus being the first work to our best knowledge that considers video streaming over erasure-coded distributed cloud systems. The download time of each coded chunk of each video segment is characterized and ordered statistics over the choice of the erasure-coded chunks is used to obtain the playback time of different video segments. Using the playback times, bounds on the moment generating function on the stall duration is used to bound the mean stall duration. Moment generating function based bounds on the ordered statistics are also used to bound the stall duration tail probability which determines the probability that the stall time is greater than a pre-defined number. These two metrics, mean stall duration and the stall duration tail probability, are important quality of experience (QoE) measures for the end users. Based on these metrics, we formulate an optimization problem to jointly minimize the convex combination of both the QoE metrics averaged over all requests over the placement and access of the video content. The non-convex problem is solved using an efficient iterative algorithm. Numerical results show significant improvement in QoE metrics for cloud-based video as compared to the considered baselines.

show abstract

“…Video Streaming over Cloud: Servicing Video on Demand and Live TV Content from cloud servers have been studied widely [24]- [28]. The placement of content and resource optimization over the cloud servers have been considered.…”

Section: Related Workmentioning

confidence: 99%

Optimized Video Streaming over Cloud: A Stall-Quality Trade-off

Alabbasi,

Aggarwal

2018

Preprint

View full text Add to dashboard Cite

As video-streaming services have expanded and improved, cloud-based video has evolved into a necessary feature of any successful business for reaching internal and external audiences. In this paper, video streaming over distributed storage is considered where the video segments are encoded using an erasure code for better reliability. There are multiple parallel streams between each server and the edge router. For each client request, we need to determine the subset of servers to get the data, as well as one of the parallel stream from each chosen server. In order to have this scheduling, this paper proposes a two-stage probabilistic scheduling. The selection of video quality is also chosen with a certain probability distribution. With these parameters, the playback time of video segments is determined by characterizing the download time of each coded chunk for each video segment. Using the playback times, a bound on the moment generating function of the stall duration is used to bound the mean stall duration. Based on this, we formulate an optimization problem to jointly optimize the convex combination of mean stall duration and average video quality for all requests, where the two-stage probabilistic scheduling, probabilistic video quality selection, bandwidth split among parallel streams, and auxiliary bound parameters can be chosen. This non-convex problem is solved using an efficient iterative algorithm. Evaluation results show significant improvement in QoE metrics for cloud-based video as compared to the considered baselines.

show abstract

Implementation of cloud based live streaming for surveillance

Cited by 11 publications

References 5 publications

Modeling and Optimization of Latency in Erasure-coded Storage Systems

Modeling and Optimization of Latency in Erasure-coded Storage Systems

Video Streaming in Distributed Erasure-Coded Storage Systems: Stall Duration Analysis

Optimized Video Streaming over Cloud: A Stall-Quality Trade-off

Contact Info

Product

Resources

About