Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.future.2015.06.001 | Future Generation Computer Systems |
Keywords | Field | DocType |
Network tomography,Network health,Cloud computing | Iterative reconstruction,Medical imaging,e-Science,Computer science,Health indicator,Testbed,Network tomography,Distributed computing,Network performance,Cloud computing | Journal |
Volume | ISSN | Citations |
56 | 0167-739X | 6 |
PageRank | References | Authors |
0.49 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan Chard | 1 | 105 | 12.60 |
| Kris Bubendorfer | 2 | 341 | 29.28 |
| Bryan Ng | 3 | 100 | 20.84 |