LAUSR

Leveraging emerging edge computing and 5G networks, researchers proposed to offload the 3D rendering of interactive multimedia applications (e.g. virtual reality and cloud gaming) onto edge servers. For high resource utilization, multiple rendering tasks run in the same GPU server and compete against each other for the computation resource. Each task has its requirement for performance, i.e. QoS target. A significant problem is how to schedule tasks so that each preset QoS is met and the performance of all tasks are maximized. We make the following contributions. First, we formulate the problem into a QoS constrained max-min utility problem. Second, we find that using the common natural logarithm as a utility function overly promotes one performance but demotes another. To avoid this phenomenon, we design a special utility function. Third, we propose an efficient scheduling algorithm, consisting of a resolution adjustment algorithm and a frame rate fair scheduling algorithm, both of which interact with each other. The former selects resolutions for tasks and the latter decides which task to process. We evaluate our method with actual rendering data, and the simulations demonstrate that our method can effectively improve task performance as well as satisfy QoS simultaneously.