Serving large-scale language models (LLMs) requires significant system resources, where GPU memory limits, system bottlenecks, and I/O delays collectively introduce substantial latency. We present MBALL (Memory BALLooner), a lightweight framework that jointly optimizes resource allocation across VRAM, DRAM, and NVMe storage to enable cost-efficient and sustainable inference. MBALL integrates task-aware scheduling and cooperative memory management to mitigate contention in real deployments. Experiments with state-of-the-art LLM workloads show that MBALL improves throughput by up to 60%, reduces latency by 35%, and lowers VRAM usage by 15%; results represent means over n=5 runs with 95% confidence intervals. MBALL provides a flexible design applicable across diverse industrial domains, including smart logistics, real-time customer services, and AI-driven IoT platforms, and offers empirical insights that can inform future system-level LLM optimization research. This paper describes the MBALL architecture and its core components, evaluates its performance against strong baselines, and demonstrates how it enables scalable and practical AI services in real deployments.

Existing memory reclamation policies on mobile devices may be no longer valid because they have negative effects on the response time of running applications. In this paper, we propose SWAM, a new integrated memory management technique that complements the shortcomings of both the swapping and killing mechanism in mobile devices and improves the application responsiveness. SWAM consists of (1) Adaptive Swap that performs swapping adaptively into memory or storage device while managing the swap space dynamically, (2) OOM Cleaner that reclaims shared object pages in the swap space to secure available memory and storage space, and (3) EOOM Killer that terminates processes in the worst case while prioritizing the lowest initialization cost applications as victim processes first. Experimental results demonstrate that SWAM significantly reduces the number of applications killed by OOMK (6.5x lower), and improves application launch time (36% faster) and response time (41% faster), compared to the conventional schemes.

Unconventional computing platforms have spread widely and rapidly following smart phones and tablets: consumer electronics such as smart TVs and digital cameras. For such devices, fast booting is a critical requirement; waiting tens of seconds for a TV or a camera to boot up is not acceptable, unlike a PC or smart phone. Moreover, the software platforms of these devices have become as rich as conventional computing devices to provide comparable services. As a result, the booting procedure to start every required OS service, hardware component, and application, the quantity of which is ever increasing, may take unbearable time for most consumers. To accelerate booting, this paper introduces Booting Booster (BB), which is used in all 2015 Samsung Smart TV models, and which runs the Linux-based Tizen OS. BB addresses the init scheme of Linux, which launches initial user-space OS services and applications and manages the life cycles of all user processes, by identifying and isolating booting-critical tasks, deferring non-critical tasks, and enabling execution of more tasks in parallel. BB has been successfully deployed in Samsung Smart TV 2015 models achieving a cold boot in 3.5 s (compared to 8.1 s with full commercial-grade optimizations without BB) without the need for suspend-to-RAM or hibernation. After this successful deployment, we have released the source code via http://opensource.samsung.com/, and BB will be included in the open-source OS, Tizen (http://tizen.org/).