File I/O buffer caching plays an important role to narrow the wide speed gap between main memory and secondary storage. However, data loss or inconsistency may occur if the system crashes before updated data in the buffer cache is flushed to storage. Thus, most operating systems adopt a daemon that periodically flushes dirty data to secondary storage. This periodic flush degrades the caching efficiency seriously because most write requests lead to direct storage accesses. We show that periodic flush accounts for 30-70 percent of the total write traffic to storage. To remove this inefficiency, this paper presents a new buffer cache architecture that adopts a small amount of non-volatile memory to maintain modified data. This novel buffer cache architecture removes almost all storage accesses due to periodic flush operations without any loss of reliability. It also improves the buffer cache performance through space-efficient management techniques, such as delta-write and fragment-grouping. Our experimental results show that the proposed buffer cache reduces the storage write traffic by 44.3 percent and also improves the throughput by 23.6 percent on average.

Medication errors significantly impact patient health and quality of care. In this study, systems modeling and simulation are used to analyze the medication workflow in a pediatric setting. The resulting simulation system allows for derivation and validation of effective intervention strategies for error mitigation. The analysis focused on ‘High Alert’ medications, which are more likely to produce serious patient harm when errors occur. Our study shows that strategic process interventions (e.g., independent intervention check points) can significantly reduce error occurrence (by as much as 50.25%) and the cost savings could be significant. We analyze the cost-savings versus intervention effectiveness versus the cost of the extra resource needed to perform the intervention, and identify the break-even point. The estimated errors from our model remain consistently close to the reported error statistics (within 5%). This work offers a system-decision support framework for analysis of medication workflow and helps in understanding error propagation mechanisms and process interdependencies. The framework allows users to derive intervention strategies and evaluate their overall mitigation effectiveness.