Name
Abstract | ||
|---|---|---|
During the last decade, multiple applications and services have been migrated to Cloud computing infrastructures. Cloud infrastructures offer flexibility in terms of the variety of applications they can service. Moreover, integrity of data and virtually unlimited storage space are attractive features, especially for end-users requiring massive amounts of storage. Recently, many HPC applications have also been migrated to the Cloud. Such applications include Oil and Gas exploration, Genomics and Ray-tracing. However, the problem of underutilization of computational resources, as well as the choice of adequate computational equipment as a function of input data, computational work, pricing and energy consumption poses a major problem in modern Cloud environment. A technique for the characterization of hardware with respect to application and hardware parameters, e.g. computational efficiency versus power consumption is proposed. The technique is based on indexes built upon ratios to baseline hardware with respect to three of the applications involved in the CloudLightning project: Oil and Gas, Ray-Tracing, Dense and Sparse matrix Computations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/3003733.3003749 | PCI |
Keywords | Field | DocType |
Cloud computing, self-management, self-organization, Cloud HPC, Cloud Lightning | Sparse matrix computations,Computer science,Self-organization,Fossil fuel,Computer hardware,Energy consumption,Cloud testing,Power consumption,Cloud computing,Distributed computing | Conference |
Citations | PageRank | References |
1 | 0.35 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. K. Filelis-Papadopoulos | 1 | 21 | 8.98 |
| E.-N. G. Grylonakis | 2 | 3 | 1.73 |
| Kyziropoulos, P.E. | 3 | 11 | 2.37 |
| George A. Gravvanis | 4 | 396 | 90.93 |
| John P. Morrison | 5 | 262 | 45.28 |