Mobile Augmented Reality (MAR) applications usually contain computation-intensive tasks which far outstrip the capability of mobile devices. One way to overcome this is offloading computation-intensive MAR tasks to remote clouds. However, the wide area network delay is hard to reduce. Thanks to edge computing, we can offload MAR tasks to nearby servers. Prior studies focus on either single-task MAR applications offloading or dependent tasks offloading for a single user. In this article, we study the offloading decision of MAR applications from multiple users, each of which is comprised of a chain of dependent tasks, over a generic cloud-edge system consisting of a group of heterogeneous edge servers and remote clouds. We formulate the Multi-user Multi-task MAR Application Scheduling (M ³ AS) problem, which is NP-hard. We present Mutas, an efficient scheduling algorithm that jointly optimizes server assignment and resource management. We also consider the online version of M ³ AS and present OnMutas. Extensive evaluations demonstrate that both Mutas and OnMutas can significantly reduce the service delays of MAR applications when compared to three other heuristics.

中文翻译：

---

基于边缘的MAR系统的联合服务器分配和资源管理

移动增强现实（MAR）应用程序通常包含计算密集型任务，远远超出了移动设备的功能。解决此问题的一种方法是将计算密集型MAR任务卸载到远程云。但是，广域网延迟很难降低。借助边缘计算，我们可以将MAR任务卸载到附近的服务器。先前的研究集中于单个用户的MAR的单任务卸载或相关任务的卸载。在本文中，我们研究了在由一组异类边缘服务器和远程云组成的通用云边缘系统上，MAR用户从多个用户的卸载决策，每个用户均由一系列相关任务组成。我们制定了多用户多任务MAR应用计划（M ³ AS）问题，这是NP难题。我们提出了Mutas，这是一种有效的调度算法，可以共同优化服务器分配和资源管理。我们还将考虑M ³ AS的在线版本 以及当前的OnM​​utas。广泛的评估表明，与其他三种启发式方法相比，Mutas和OnMutas都可以显着减少MAR应用程序的服务延迟。