In Cloud, Can Scientific Communities Benefit from the Economies of Scale?

TL;DR

This paper introduces DawningCloud, a system that enables small and medium scientific communities to leverage cloud economies of scale, significantly reducing resource consumption for workloads like HTC and MTC.

Contribution

It proposes an enhanced cloud model (ESP), implements the DawningCloud system, and develops an emulation methodology for evaluating resource efficiency in scientific workloads.

Findings

DawningCloud reduces resource consumption by up to 44.5% for HTC workloads.

DawningCloud reduces resource consumption by up to 72.6% for MTC workloads.

Total resource savings reach up to 47.3% compared to previous solutions.

Abstract

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.