Network health and e-Science in commercial clouds

•We identify and characterize network performance in commercial clouds.•An overall health system is constructed using tomographic probes to establish and compare an instance’s network performance.•We deploy the health system over a testbed of 100 AWS instances and explore its ability to scale.•We apply the health system to a medical imaging e-Science application and demonstrate performance benefits.

This paper explores the potential for improving the performance of e-Science applications on commercial clouds through the detailed examination, and characterization, of the underlying cloud network using network tomography. Commercial cloud providers are increasingly offering high performance and GPU-enabled resources that are ideal for many e-Science applications. However, the opacity of the cloud’s internal network, while a necessity for elasticity, limits the options for e-Science programmers to build efficient and high performance codes. We introduce health indicators, markers, metrics, and score as part of a network health system that provides a model for describing the overall network health of an e-Science application. We then explore the suitability of a range of tomographic techniques to act as health indicators using two testbeds—the second of which spanned one hundred AWS instances. Finally, we evaluate our work using a real-world medical image reconstruction application.