The Taiwan Centers for Disease Control (Taiwan CDC) has established a 3-tier personal protective equipment (PPE) stockpiling framework that could maintain a minimum stockpile for the surge demand of PPE in the early stage of a pandemic. However, PPE stockpiling efforts must contend with increasing storage fees and expiration problems. In 2011, the Taiwan CDC initiated a stockpile replacement model in order to optimize the PPE stockpiling efficiency, ensure a minimum stockpile, use the government's limited funds more effectively, and achieve the goal of sustainable management. This stockpile replacement model employs a first-in-first-out principle in which the oldest stock in the central government stockpile is regularly replaced and replenished with the same amount of new and qualified products, ensuring the availability and maintenance of the minimum stockpiles. In addition, a joint electronic procurement platform has been established for merchandising the replaced PPE to local health authorities and medical and other institutions for their routine or epidemic use. In this article, we describe the PPE stockpile model in Taiwan, including the 3-tier stockpiling framework, the operational model, the components of the replacement system, implementation outcomes, epidemic supports, and the challenges and prospects of this model.
Due to the increasing number of computer hosts deployed in an enterprise, automatic management of electronic applications is inevitable. To provide diverse services, there will be increases in procurement, maintenance, and electricity costs. Virtualization technology is getting popular in cloud computing environment, which enables the efficient use of computing resources and reduces the operating cost. In this paper, we present an automatic mechanism to consolidate virtual servers and shut down the idle physical machines during the off-peak hours, while activating more machines at peak times. Through the monitoring of system resources, heavy system loads can be evenly distributed over physical machines to achieve load balancing. By integrating the feature of load balancing with virtual machine live migration, we successfully develop an automatic private cloud management system. Experimental results demonstrate that, during the off-peak hours, we can save power consumption of about 69 W by consolidating the idle virtual servers. And the load balancing implementation has shown that two machines with 80% and 40% CPU loads can be uniformly balanced to 60% each. And, through the use of preallocated virtual machine images, the proposed mechanism can be easily applied to a large amount of physical machines.
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