The paper shows that task scheduling in Cloud VMs hasn't evolved quickly to handle the dynamic vCPU resources. The existing vCPU abstraction cannot accurately depict the vCPU dynamics in capacity, activity, and topology, and these mismatches can mislead the scheduler, causing performance degradation and system anomalies. The paper proposes a novel solution, vSched, which probes accurate vCPU abstraction through a set of lightweight microbenchmarks (vProbers) without modifying the hypervisor, and leverages the probed information to optimize task scheduling in cloud VMs with three new techniques: biased vCPU selection, intra-VM harvesting, and relaxed work conservation. Our evaluation of vSched's implementation in x86 Linux Kernel demonstrates that it can effectively improve both system throughput and workload latency across various VM types in the dynamic multi-cloud environment.

Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from >450 patients as part of an ongoing clinical study.