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.