Name
Abstract | ||
|---|---|---|
Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DSN-W.2016.58 | 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W) |
Keywords | Field | DocType |
profiling memory vulnerability,cloud computing,in-memory Big-data analytics,memory subsystems,cache bandwidth,memory bandwidth,spark applications | Spark (mathematics),Memory bandwidth,Cache,Computer science,Profiling (computer programming),Bandwidth (signal processing),Analytics,Big data,Cloud computing,Distributed computing | Conference |
ISSN | ISBN | Citations |
2325-6648 | 978-1-5090-3688-2 | 0 |
PageRank | References | Authors |
0.34 | 1 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Navaneeth Rameshan | 1 | 29 | 3.93 |
| Robert Birke | 2 | 133 | 15.51 |
| Leandro Navarro | 3 | 87 | 10.60 |
| Vladimir Vlassov | 4 | 23 | 2.38 |
| Bhuvan Urgaonkar | 5 | 2309 | 158.10 |
| George Kesidis | 6 | 293 | 38.77 |
| Martin L. Schmatz | 7 | 155 | 26.29 |
| Lydia Y. Chen | 8 | 432 | 52.24 |