A smart TV has been changed from an appliance which handles just multimedia contents to a high tech device that provides more various and valuable services based on the fast network and the multicore environment. Furthermore, a smart TV is expected to play a role as a home server which cooperates with other IT appliances. For this reason, the importance of analyzing the various workloads and understanding the properties in the systems has increased. It is hard to meet user's requirements in the formal hardware resource and providing many services simultaneously cause the resource limitation for operating smart TV systems, i.e. it is hard to provide various services smoothly.In this paper, we analyze the smart TV workload resource consumption properties and user's request response latency problem when some workloads performed at the same time. Then, we propose cgroups-based CPU scheduling scheme using cgroups. This scheduling scheme guarantees response to user's request and the video quality.
Today, the virtualization is a very important technology which is widely used in various area, from small mobile devices to virtual machine (VM) servers for large scale cloud computing. Now, hypervisor provides CPU and memory resources for the VMs with high performance like native machine through many researches on the virtualized environment. However, device virtualization techniques, especially those for GPU devices, are less studied than the other virtualization techniques. It is a chief obstacle to perform graphics processing in the virtualized environment. Since VM cannot access the physical GPU device directly, existing GPU device virtualization techniques have some limitations on 3D acceleration. Especially, those techniques spend more time to perform graphics processing because they use software rendering on the Mesa Software Fallback module in the guest OS. In this paper, we propose a GPU device virtualization technique that can improve OpenGL graphics performance. By using concurrent I/O request queue between the host emulation process and the guest OS, GPU device can be accessed directly. Our scheme can avoid graphics processing in the graphics stack of the guest OS and also can reduce vmexit overheads. The emulation process can perform the graphics processing using GPU hardware rendering. Our evaluation shows that the proposed technique has about 2.5x higher frame rate than existing Mesa software rendering.
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