  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.
