Traditional GPU resource allocation approaches, widely adopted in today’s data centers as the rise of cloud gaming, only focus on server-side functions while ignoring the client-side, which wastes hardware resources. To solve this problem, cloud-edge integrated architectures are put forward, leaving some workloads to the client-side. However, many cloud-edge integrated frameworks suffer from one big issue: shared-resource interference, stemming from two reasons: (a) GPU resource racing caused by resources overuse for single client, and (b) CPU resource racing caused by resources shortage among clients.

This paper presents gRemote, an open-source cloud-rendering system that can address these issues on a GPU resource pool. To mitigate the CPU resource shortage, gRemote improves CPU configuration by expanding CPU resources from server-side to both server- and client-side. To maintain the reasonable GPU usage for individual tasks, gRemote innovates a new resource-sharing mechanism called GPU throttle. Furthermore, remote API-Forwarding brings another type of network bandwidth consumption: command streaming. To optimize the network bandwidth for GPU command transmission, gRemote proposes two methods, command characteristics-oriented (CCO) compression algorithm and command-transmitted methodology. gRemote supports 1,228 OpenGL commands and provides cloud rendering for more than 40 clients with negligible shared-resource interference. With CCO algorithm and command-transmitted methodology, the network bandwidth is saved by more than 90%.

随着GPU游戏的兴起，在当今的数据中心中广泛采用的传统GPU资源分配方法只专注于服务器端功能，而忽略了客户端，这浪费了硬件资源。为了解决这个问题，提出了云边缘集成架构，将一些工作负载留给了客户端。但是，许多云边缘集成框架都存在一个大问题：共享资源干扰，其原因有两个：（a）由单个客户端的资源过度使用引起的GPU资源竞争，以及（b）由于资源不足导致的CPU资源竞争客户。

本文介绍了gRemote，这是一个开源的云渲染系统，可以在GPU资源池上解决这些问题。为了减轻CPU资源的短缺，gRemote通过将CPU资源从服务器端扩展到服务器端和客户端来改善CPU配置。为了在单个任务上保持合理的GPU使用率，gRemote创新了一种称为GPU节流的新资源共享机制。此外，远程API转发带来了另一种类型的网络带宽消耗：命令流。为了优化用于GPU命令传输的网络带宽，gRemote提出了两种方法，即面向命令特征（CCO）的压缩算法和命令发送方法。gRemote支持1,228条OpenGL命令，并为40多个客户端提供了云渲染，并且共享资源的干扰可忽略不计。借助CCO算法和命令传输方法，可以节省90％以上的网络带宽。