In Cloud, Can Scientific Communities Benefit from the Economies of Scale?
Parallel And Distributed System
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 research organizations rent elastic resources from a public cloud provider; second, on a basis of the ESP model, we design and implement the Dawning Cloud 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), Dawning Cloud 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, Dawning Cloud can enable scientific communities to benefit from the economies of scale of public Clouds.