In the realm of edge computing, the increasing demand for high Quality of Service (QoS), particularly in dynamic multimedia streaming applications (e.g., Augmented Reality/Virtual Reality and online gaming), has prompted the need for effective solutions. Nevertheless, adopting an edge paradigm grounded in distributed computing has exacerbated the issue of tail latency. Given a limited variety of multimedia services supported by edge servers and the dynamic nature of user requests, employing traditional queuing methods to model tail latency in distributed edge computing is challenging, substantially exacerbating head-of-line (HoL) blocking. In response to this challenge, we have developed a learning-based scheduling method to mitigate the overall tail latency, which adaptively selects appropriate edge servers for execution as incoming distributed tasks vary with unknown size. To optimize the utilization of the edge computing paradigm, we leverage Laplace transform techniques to theoretically derive an upper bound for the response time of edge servers. Subsequently, we integrate this upper bound into reinforcement learning to facilitate tail learning and enable informed decisions for autonomous distributed scheduling. The experiment results demonstrate the efficiency in reducing tail latency compared to existing methods.

翻译：在边缘计算领域，对高服务质量（QoS）的日益增长需求——特别是在动态多媒体流式应用（如增强现实/虚拟现实和在线游戏）中——催生了有效解决方案的需求。然而，采用基于分布式计算的边缘范式加剧了尾部延迟问题。考虑到边缘服务器支持的有限种类多媒体服务以及用户请求的动态特性，使用传统排队方法对分布式边缘计算中的尾部延迟建模存在挑战，显著加剧了队头阻塞。针对这一挑战，我们开发了一种基于学习的调度方法以缓解总体尾部延迟，该方法能在传入的分布式任务大小未知且动态变化时，自适应地选择合适的边缘服务器执行任务。为优化边缘计算范式的利用，我们利用拉普拉斯变换技术从理论上推导边缘服务器响应时间的上界。随后，我们将该上界集成到强化学习中，以促进尾延迟学习，并为自主分布式调度实现知情决策。实验结果表明，与现有方法相比，该方法在降低尾部延迟方面具有显著效果。