2 research outputs found
Container Resource Allocation versus Performance of Data-intensive Applications on Different Cloud Servers
In recent years, data-intensive applications have been increasingly deployed
on cloud systems. Such applications utilize significant compute, memory, and
I/O resources to process large volumes of data. Optimizing the performance and
cost-efficiency for such applications is a non-trivial problem. The problem
becomes even more challenging with the increasing use of containers, which are
popular due to their lower operational overheads and faster boot speed at the
cost of weaker resource assurances for the hosted applications. In this paper,
two containerized data-intensive applications with very different performance
objectives and resource needs were studied on cloud servers with Docker
containers running on Intel Xeon E5 and AMD EPYC Rome multi-core processors
with a range of CPU, memory, and I/O configurations. Primary findings from our
experiments include: 1) Allocating multiple cores to a compute-intensive
application can improve performance, but only if the cores do not contend for
the same caches, and the optimal core counts depend on the specific workload;
2) allocating more memory to a memory-intensive application than its
deterministic data workload does not further improve performance; however, 3)
having multiple such memory-intensive containers on the same server can lead to
cache and memory bus contention leading to significant and volatile performance
degradation. The comparative observations on Intel and AMD servers provided
insights into trade-offs between larger numbers of distributed chiplets
interconnected with higher speed buses (AMD) and larger numbers of centrally
integrated cores and caches with lesser speed buses (Intel). For the two types
of applications studied, the more distributed caches and faster data buses have
benefited the deployment of larger numbers of containers
Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study
BACKGROUND: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. OBJECTIVE: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. METHODS: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. RESULTS: The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P\u3c.001). CONCLUSIONS: This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation