A flexible platform for QoE-driven delivery of image-rich web applications

P. Ahammad, R. Gaunker, B. Kennedy, Mehrdad Reshadi, K. Kumar, A. K. Pathan, Hariharan Kolam
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引用次数: 7

Abstract

The advent of content-rich modern web applications, unreliable network connectivity and device heterogeneity demands flexible web content delivery platforms that can handle the high variability along many dimensions - especially for the mobile web. Images account for more than 60% of the content delivered by present-day webpages and have a strong influence on the perceived webpage latency and end-user experience. We present a flexible web delivery platform with a client-cloud architecture and content-aware optimizations to address the problem of delivering image-rich web applications. Our solution makes use of quantitative measures of image perceptual quality, machine learning algorithms, partial caching and opportunistic client-side choices to efficiently deliver images on the web. Using data from the WWW, we experimentally demonstrate that our approach shows significant improvement on various web performance criteria that are critical for maintaining a desirable end-user quality-of-experience (QoE) for image-rich web applications.
一个灵活的平台,用于qos驱动的图像丰富的web应用程序交付
内容丰富的现代web应用程序的出现,不可靠的网络连接和设备的异构性要求灵活的web内容交付平台,可以处理在许多维度上的高度可变性-特别是对于移动web。图片占当前网页内容的60%以上,对网页延迟和最终用户体验有很大的影响。我们提供了一个灵活的web交付平台,它具有客户端云架构和内容感知优化,以解决交付图像丰富的web应用程序的问题。我们的解决方案利用图像感知质量的定量测量、机器学习算法、部分缓存和机会主义的客户端选择来有效地在网络上传递图像。使用来自WWW的数据,我们通过实验证明,我们的方法在各种web性能标准上显示出显著的改进,这些标准对于维护理想的终端用户体验质量(QoE)对于图像丰富的web应用程序至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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