论文标题
云计算中虚拟桌面基础架构的高效视频流式体系结构
An Efficient Video Streaming Architecture with QoS Control for Virtual Desktop Infrastructure in Cloud Computing
论文作者
论文摘要
在虚拟桌面基础架构(VDI)环境中,远程显示协议有很大的责任将视频数据从数据中心托管的桌面传输到端点。该协议必须确保在繁重的工作负载条件下,高水平的客户感知到的端到端服务质量(QoS)。每个远程显示协议的工作方式都不同,具体取决于网络以及提供哪些应用程序。在医疗保健应用中,医生和护士可以直接使用移动设备来监测患者。此外,实施需要大量消耗CPU和其他资源的任务的能力适用于包括研究和云游戏在内的各种应用程序。此类计算机游戏和复杂的过程将在功能强大的云服务器上运行,并且屏幕内容将传输到客户端。为了启用此类应用程序,远程显示技术需要进一步的增强功能,以满足带宽和QoS的更严格的要求,从而允许实时操作。在本文中,我们提出了一个架构,包括灵活的QoS控制,以提高体验用户质量(QOE)。 QoS控制是基于使用历史网络数据的线性回归建模开发的。此外,该体系结构还包括2D图像的新型压缩算法,旨在保证最佳图像质量和减少视频延迟;该算法基于K-均值聚类,可以满足实时板载处理的要求。通过MIT计算机科学和人工实验室收集的真实工作数据集的模拟,我们介绍了实验并解释QoS系统的性能。
In virtual desktop infrastructure (VDI) environments, the remote display protocol has a big responsibility to transmit video data from a data center-hosted desktop to the endpoint. The protocol must ensure a high level of client perceived end-to-end quality of service (QoS) under heavy work load conditions. Each remote display protocol works differently depending on the network and which applications are being delivered. In healthcare applications, doctors and nurses can use mobile devices directly to monitor patients. Moreover, the ability to implement tasks requiring high consumption of CPU and other resources is applicable to a variety of applications including research and cloud gaming. Such computer games and complex processes will run on powerful cloud servers and the screen contents will be transmitted to the client. TO enable such applications, remote display technology requires further enhancements to meet more stringent requirements on bandwidth and QoS, an to allow realtime operation. In this paper, we present an architecture including flexible QoS control to improve the user quality of experience (QoE). The QoS control is developed based on linear regression modeling using historical network data. Additionally, the architecture includes a novel compression algorithm of 2D images, designed to guarantee the best image quality and to reduce video delay; this algorithm is based on k-means clustering and can satisfy the requirements of realtime onboard processing. Through simulations with a real work dataset collected by the MIT Computer Science and Artificial Lab, we present experimental as well as explain the performance of the QoS system.