Desktop-as-a-service (DaaS) has been recognized as an elastic and economical solution that enables users to access personal desktops from anywhere at any time. During the interaction process of DaaS, users rely on screen updates to perceive execution results remotely, and thus the reliability and timeliness of screen updates transmission have a great influence on users’ quality of experience (QoE). However, the efficient transmission of screen updates in DaaS is facing severe challenges: most transmission schemes applied in DaaS determine sending strategies in terms of pre-set rules, lacking the intelligence to utilize bandwidth rationally and fit new network scenarios. Meanwhile, they tend to focus on reliability or timeliness and perform unsatisfactorily in ensuring reliability and timeliness simultaneously, leading to lower transmission efficiency of screen updates and users’ QoE when network conditions turn unfavorable. In this article, an intelligent and reliable end-to-end transmission scheme (IRTS) is proposed to cope with the preceding issues. IRTS draws support from reinforcement learning by adopting SARSA, an online learning method based on the temporal difference update rule, to grasp the optimal mapping between network states and sending actions, which extricates IRTS from the reliance on pre-set rules and augments its adaptability to different network conditions. Moreover, IRTS guarantees reliability and timeliness via an adaptive loss recovery method, which intends to recover lost screen updates data automatically with fountain code while controlling the number of redundant packets generated. Extensive performance evaluations are conducted, and numerical results show that IRTS outperforms the reference schemes in display quality, end-to-end delay/delay jitter, and fairness when transferring screen updates under various network conditions, proving that IRTS can enhance the transmission efficiency of screen updates and users’ QoE in DaaS.

中文翻译：

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IRTS：DaaS 中用于屏幕更新交付的智能可靠传输方案

桌面即服务 (DaaS) 已被公认为一种弹性且经济的解决方案，它使用户能够随时随地访问个人桌面。在DaaS交互过程中，用户依靠屏幕更新远程感知执行结果，因此屏幕更新传输的可靠性和及时性对用户的体验质量（QoE）影响很大。然而，DaaS 中屏幕更新的高效传输面临严峻挑战：DaaS 中应用的传输方案大多根据预先设定的规则确定发送策略，缺乏合理利用带宽和适应新网络场景的智能。同时，他们倾向于关注可靠性或及时性，在同时确保可靠性和及时性方面表现不佳，当网络条件不利时，导致屏幕更新传输效率和用户体验质量下降。针对上述问题，本文提出了一种智能可靠的端到端传输方案（IRTS）。IRTS 在强化学习的支持下，采用基于时间差更新规则的在线学习方法 SARSA 来掌握网络状态和发送动作之间的最优映射，从而使 IRTS 摆脱了对预设规则的依赖，增强了其对网络状态的适应性。不同的网络条件。此外，IRTS通过自适应丢失恢复方法保证可靠性和及时性，该方法旨在通过喷泉码自动恢复丢失的屏幕更新数据，同时控制生成的冗余数据包的数量。