Most modern operating systems have adopted the one-to-one thread model to support fast execution of threads in both multi-core and single-core systems. This thread model, which maps the kernel-space and user-space threads in a one-to-one manner, supports quick thread creation and termination in high-performance server environments. However, the performance of time-critical threads is degraded when multiple threads are being run in low-end CE devices with limited system resources. When a CE device runs many threads to support diverse application functionalities, low-level hardware specifications often lead to significant resource contention among the threads trying to obtain system resources. As a result, the operating system encounters challenges, such as excessive thread context switching overhead, execution delay of time-critical threads, and a lack of virtual memory for thread stacks. This article proposes a state-of-the-art Thread Evolution Kit (TEK) that consists of three primary components: a CPU Mediator, Stack Tuner, and Enhanced Thread Identifier. From the experiment, we can see that the proposed scheme significantly improves user responsiveness (7× faster) under high CPU contention compared to the traditional thread model. Also, TEK solves the segmentation fault problem that frequently occurs when a CE application increases the number of threads during its execution.

Multi-user surface computing systems are promising for the next generation consumer electronics devices due to their convenience and excellent usability. However, conventional resource scheduling schemes can cause severe performance issues in surface computing systems, especially when they are adopted in multi-user environments, because they do not consider the characteristics of multi-user surface computing systems. In this paper, we propose an efficient user-based resource scheduling scheme for multi-user surface computing systems, called URS. URS provides three different features to effectively support multi-user surface computing systems. First, URS distributes system resources to users, according to their real-world priorities, rather than processes or tasks. Second, URS provides performance isolation among multiple users to prevent resource monopoly by a single user. Finally, URS prioritizes a foreground application of each user via retaining pages used by the application in the page cache, in order to enhance multi-user experience. Our experimental results confirm that URS effectively allocates system resources to multiple users by providing the aforementioned three features.