在云计算中，科学社区可以从规模经济中受益吗？
In Cloud, Can Scientific Communities Benefit from the Economies of Scale?
摘要：
云计算背后的基本思想是资源提供者为最终用户提供弹性资源。在本文中，我们打算回答一个关键问题，云计算的成功：在云，小型或中型规模的科学计算社区可以受益于规模经济？我们的研究贡献有三个方面：第一，我们提出了一种增强型的科学公有云模型（ESP），鼓励中小型组织向公有云提供商租用弹性资源;第二，在ESP模型的基础上，我们设计并实现了曙光云系统，可以将异构的科学工作负载整合到云站点上;第三，提出了创新的仿真方法，并进行了综合评价。我们发现，对于高吞吐量计算（HTC）和多任务计算（MTC）两种典型工作负载，曙光云相对于前两种公有云解决方案，为服务提供商最大节省了44.5%（HTC）和72.6%（MTC）的资源消耗，为资源提供商最大节省了47.3%的总资源消耗。为此，我们得出结论，对于典型的工作负载：HTC和MTC，DawningCloud可以使科学界从公共云的规模经济中受益。
英文摘要：
The basic idea behind Cloud computing is that resource providers offer elastic resources to end users. In this paper, we intend to answer one key question to the success of Cloud computing: in Cloud, can small or medium-scale scientific computing communities benefit from the economies of scale? Our research contributions are three-fold: first, we propose an enhanced scientific public cloud model (ESP) that encourages small- or medium-scale organizations to rent elastic resources from a public cloud provider; second, on a basis of the ESP model, we design and implement the DawningCloud system that can consolidate heterogeneous scientific workloads on a Cloud site; third, we propose an innovative emulation methodology and perform a comprehensive evaluation. We found that for two typical workloads: high throughput computing (HTC) and many task computing (MTC), DawningCloud saves the resource consumption maximally by 44.5% (HTC) and 72.6% (MTC) for service providers, and saves the total resource consumption maximally by 47.3% for a resource provider with respect to the previous two public Cloud solutions. To this end, we conclude that for typical workloads: HTC and MTC, DawningCloud can enable scientific communities to benefit from the economies of scale of public Clouds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
