RESUMEN
Background: Hospital administrators are often challenged with overcrowding at hospitals. The study hospital receives referred patients; however, they have to wait in long queues even for getting registered. This was a cause of concern for hospital administrators. The study was undertaken to find an amicable solution to the queues at registration using Queuing Theory. Method: This observational and interventional study was carried out in a tertiary care ophthalmic hospital. In the first phase, data of service time and arrival rate was collected. The queuing model was built using the coefficient of variation (CoV) of the observed times. Server utilization for new patient registration was found to be 1.21 and was 0.63 for revisit patients. Scenario-based simulation carried out using free software for optimal utilization of both types of servers. Recommendations made to combine the registration process and to increase one server were implemented.In the second phase, after one year, patient registration data were collected and compared for the number of patients registered using SPSS 17. Results: Number of patients registered within the registration timings increased whereas the number of patients registered after the registration timings decreased significantly at 95% CI with a p-value of less than 0.001. Queues finished early and more number of patients were registered in the same time. Conclusion: Using queuing theory, the bottleneck of the systems can be identified. Scenario and software-based simulations provide solutions to the problem of queues. The study is an application of Queuing Theory with a focus on efficient resource utilization. It can be replicated in an organization with limited resources facing the challenge of queues.
RESUMEN
The pandemic of coronavirus has dramatically disrupted the retail industry, as many stores are forced to close and people across the world are shelter-in-place with online shopping as the inevitable choice. To meet the rapidly increasing demand for e-commerce, more data centers are expected to provide new or significantly improve existing cloud services that can better support hybrid workloads (e.g. online purchase jobs and batch jobs that support ranking or recommendation systems). Successful cloud systems need to efficiently handle and quickly respond to huge volume of traffic with such hybrid workloads. Meanwhile, it is critical to reduce the total cost of ownership (TCO) for profitability. Improving system utilization is one of the effective techniques to achieve the twin goals of high performance and low TCO. This paper conducts a comprehensive analysis on the 2017 and 2018 cluster traces released by Alibaba, which provides a case study about Alibaba's best practices in improving the performance and cost efficiency of its large-scale cloud systems by consolidating time-sensitive online service jobs with time-insensitive batch jobs. Our investigation indicates that the over-subscription (causing resource waste and low utilization) and under-subscription (causing performance degradation) problems co-exist in the current Alibaba system. We develop a simulator that allows us to evaluate possible solutions to address this problem and their impact on the performance, energy consumption, and TCO. Our experiments show that the estimated TCO can be reduced by $600,000 for the 2018 trace running on over 4,000 machines without compromising performance. The TCO can decrease by nearly $68 million if similar strategy is extrapolated to Alibaba's 432,000 web facing servers.