Due to the excellent rendering capabilities, GPUs are mainstream accelerators in the Cloud-rendering industry. However, current Cloud-rendering systems suffer from a CPU-GPU workload imbalance that not only degrades application performance but also causes a significant waste of GPU resources. Recent proposals (such as API-forwarding and c-GPU) for improving CPU-GPU balance are promising but fail to solve system-resource redundancy issues (i.e., each instance tends to occupy all resources, exceeding its requirements). Such behavior will increase CPU load and lower effective GPU utilization. To demonstrate the severity of the issue, we evaluated real-world applications and results show that in most cases, nearly 50% of resources are useless. To solve this problem, we present CARE, the first framework intended to reduce the system-level redundancy by cloudifying the system from monolithic to Cloud-native. To allow users to configure required services, CARE puts forward a functional unit calledConfigurable Android (CA). To allow multiple instances to share certain types of resources, CARE innovatesSharing Resource (SR). To reduce the unused services, CARE introducesPruning Resources (PR). To further alleviate the CPU pressure and achieve CPU-GPU balance, we propose rShare, a system aiming at enhancing CPU effective utilization and increasing Android instance density of the Cloud-rendering platform. Based on Kubernetes, rShare divides all the CPUs into non-overlapping shared CPU pools, allocates instances to pools within milliseconds, and dynamically migrates them by tracking their QoS status. So far, CARE primarily focuses on Android systems and can handle 60 heavyweight instances (e.g., KOG (King of Glory)) on Intel SG1. rShare can apply instance allocation within milliseconds and increase the platform density by 39.4%.

GPUs have become ubiquitous in the Cloud-rendering areas due to the outstanding rendering performance. However, many existing Cloud-rendering systems suffer from low GPU utilization caused by the CPU bottleneck. Recent proposals (e.g., API-forwarding and c-GPU) for GPU-usage optimization are promising but fail to address the system-resource redundancy issues (i.e., each instance tends to occupy all the system resources exceeding their requirements), leading to unnecessary CPU consumption and lowering GPU utilization. We conducted an experiment by testing real-world applications on the percentage of unused resources to demonstrate the severity of this issue. Nearly 50% of resources are unused.

Cloud rendering is an emerging technology in which rendering-heavy applications run on the cloud server and then stream the rendered contents to the end-user device. High density and high scalability of the cloud rendering services are crucial to support millions of users concurrently and cost-effectively. However, it is still challenging to run Android OS in cloud smoothly with high density and high scalability without compromising user experience. This paper presents DroidCloud, the first open-source Android\footnoteAndroid is a trademark of Google LLC. cloud rendering solution focusing on the scalable design and density aspect optimization to the best of our knowledge. To cloudify Android OS, DroidCloud utilizes thevHAL technology in order to support remote devices and keep transparent to Android applications. And aFlexible rendering scheduling policy is introduced to break the boundary of GPU physical locations. Thus, both remote GPUs and local GPUs can accommodate render tasks by forwarding rendering tasks and making it possible to support multiple Android OSes with GPU acceleration. Besides, to further improve the density, DroidCloud optimizes the resource cost both in a single instance and across instances. We show that DroidCloud can run hundreds of Android OSes on a single Intel Xeon server with GPU acceleration simultaneously, increasing the density at the scale of one order of magnitude compared to current cloud gaming systems. Further experimental results demonstrate that DroidCloud can transparently run Android applications at native speed with lower CPU, memory, and storage utilization.