This paper presents a novel approach to enhance sensing capabilities in UAV-enabled MIMO-OFDM ISAC systems by leveraging UAV mobility as a mono-static radar. By integrating uniform planar arrays (UPAs) and modeling the UAV dynamics in$S E(3)$, we address key challenges such as 3D space sensing and trajectory design. We propose a target tracking scheme using extended Kalman filtering (EKF) in$S E(3)$, along with trajectory optimization based on the conditional Posterior Cramer-Rao bound (CPCRB). Numerical results demonstrate the effectiveness of the proposed trajectory design in enhancing performance of target tracking and physical parameter estimation in UAVenabled MIMO-OFDM ISAC systems.

Virtualization is a key technology in cloud computing; it can accommodate numerous guest VMs to provide transparent services, such as live migration, high availability, and rapid checkpointing. Cloud computing using virtualization allows workloads to be deployed and scaled quickly through the rapid provisioning of virtual machines on physical machines. However, I/O virtualization, particularly for networking, suffers from significant performance degradation in the presence of high-speed networking connections. In this paper, we first analyze performance challenges in network I/O virtualization and identify two problems-conventional network I/O virtualization suffers from excessive virtual interrupts to guest VMs, and the back-end driver does not efficiently use the computing resources of underlying multicore processors. To address these challenges, we propose optimization methods for enhancing the networking performance: 1) Efficient interrupt coalescing for network I/O virtualization and 2) virtual receive-side scaling to effectively leverage multicore processors. These methods are implemented and evaluated with extensive performance tests on a Xen virtualization platform. Our experimental results confirm that the proposed optimizations can significantly improve network I/O virtualization performance and effectively solve the performance challenges.

Virtualization is a fundamental component in cloud computing because it provides numerous guest VM transparent services, such as live migration, high availability, rapid checkpoint, etc. However, I/O virtualization, particularly for network, is still suffering from significant performance degradation. In this paper, we analyze performance challenges in network I/O virtualization and observe that the conventional network I/O virtualization incurs excessive virtual interrupts to guest VMs, and the backend driver in the driver domain is not parallelized and cannot leverage underlying multi-core processors. Motivated by the above observations, we propose optimizations: efficient interrupt coalescing for network I/O virtualization and virtual receive side scaling to effectively leverage multi-core processors. We implemented those optimizations in Xen and did extensive performance evaluation. Our experimental results reveal that the proposed optimizations significantly improve network I/O virtualization performance and effectively tackle the performance challenges.