Mossad Helali, Yutai Luo, Tae Jun Ham, Jim Plotts, Ashwin Chaugule, Jichuan Chang, Parthasarathy Ranganathan, Essam Mansour. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

We present ghOSt, our infrastructure for delegating kernel scheduling decisions to userspace code. ghOSt is designed to support the rapidly evolving needs of our data center workloads and platforms.

Increasing memory demand and slowdown in technology scaling pose important challenges to total cost of ownership (TCO) of warehouse-scale computers (WSCs). One promising idea to reduce the memory TCO is to add a cheaper, but slower, "far memory" tier and use it to store infrequently accessed (or cold) data. However, introducing a far memory tier brings new challenges around dynamically responding to workload diversity and churn, minimizing stranding of capacity, and addressing brownfield (legacy) deployments. We present a novel software-defined approach to far memory that proactively compresses cold memory pages to effectively create a far memory tier in software. Our end-to-end system design encompasses new methods to define performance service-level objectives (SLOs), a mechanism to identify cold memory pages while meeting the SLO, and our implementation in the OS kernel and node agent. Additionally, we design learning-based autotuning to periodically adapt our design to fleet-wide changes without a human in the loop. Our system has been successfully deployed across Google's WSC since 2016, serving thousands of production services. Our software-defined far memory is significantly cheaper (67% or higher memory cost reduction) at relatively good access speeds (6us) and allows us to store a significant fraction of infrequently accessed data (on average, 20%), translating to significant TCO savings at warehouse scale.

Cellphones are increasingly becoming attractive targets of various malware, which not only cause privacy leakage, extra charges, and depletion of battery power, but also introduce malicious traffic into networks. In this work, we seek system-level solutions to handle these security threats. Specifically, we propose a mandatory access control-based defense to blocking malware that launch attacks through creating new processes for execution. To combat more elaborated malware which redirect program flows of normal applications to execute malicious code within a legitimate security domain, we further propose using artificial intelligence (AI) techniques such as Graphic Turing test. Through extensive experiments based on both Symbian and Linux smartphones, we show that both our system-level countermeasures effectively detect and block cellphone malware with low false positives, and can be easily deployed on existing smartphone hardware.