Instead, requests to/from the CDN are treated as service transactions in the network, which utilises a routing function embraced from emerging research in Information-Centric Networks (ICN) to route edge-to-edge transactions to the true nearest service point. We proposes a novel, flexible CDN architecture that removes the need for DNS-based mapping and content reflection. Current CDNs suffer from inefficient request mapping based on DNS redirection, and inefficient content distribution from origin to edge servers, through content reflection. įlexible and efficient CDNs are critical to facilitate content distribution in 5G+ architectures. Our study finds that: 1) cloud vendors vary in providing QoE across regions the video provider should assign a user to the CDN offering the best QoE at his location 2) the QoE provided by one CDN can change over time the video provider should adapt the CDN selection according to the real time QoE measurement 3) cloud CDNs vary in scalability streaming sessions may crash when there is bursty user demand video providers should choose among the cloud CDNs that can properly scale 4) regarding the cost, some cloud CDN is more economical than others given certain cache hit rate video providers can minimize their costs by forcing free trial users to stream from the cheapest one 1. We leverage an approximated Quality of Experience (QoE) as a metric for evaluation. We emulate video streaming users in PlanetLab cloud to measure cloud CDNs including Amazon Web Service (AWS) CloudFront, Microsoft Azure Verizon CDN, and Google Cloud CDN. The user experience and the costs of providing the same video streaming service can vary when using different cloud CDNs. We instead find that popular videos are currently well-served by the current trajectory of software encoders.Ĭloud vendors offer content delivery network (CDN) services to compete for the video market. Counterintuitively, they are not viable for popular videos, for which highly compressed, high quality copies are required. Our experiments with GPUs under vbench's scoring scenarios reveal that context is critical: GPUs are well suited for live-streaming, while for video-on-demand shift costs from compute to storage and network. vbench reveals trends from the commercial corpus that are not visible in other video corpuses. We demonstrate the importance of video selection with a microarchitectural study of cache, branch, and SIMD behavior. The combination of validated corpus, baselines, and metrics reveal nuanced tradeoffs between speed, quality, and compression. Reflecting the complex infrastructure that processes and hosts these videos, vbench includes carefully constructed metrics and baselines. ![]() Unlike prior video processing benchmarks, vbench's videos are algorithmically selected to represent a large commercial corpus of millions of videos. We are the first study, to the best of our knowledge, to characterize the emerging video-as-a-service workload. This paper presents vbench, a publicly available benchmark for cloud video services.
0 Comments
Leave a Reply. |