  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.
