Name
Abstract | ||
|---|---|---|
The lambda architectural pattern allows to overcome some limitations of data processing frameworks. It builds on the methodology of having two different data processing streams on the same system: a real time computing for fast data streams and a batch computing behavior for massive workloads for delayed processing. While these two modes are clearly not new, lambda architectures allow them to coordinate their execution to avoid interference. However resource allocation over cloud infrastructure, has greatly impacted the overall performances (and importantly costs). If performance could be modeled in advance, architects could make better judgments on allocation of their resources to use the systems more efficiently. In this paper, we present a modeling approach, based on multiformalism and multisolution techniques, that provides a fast evaluation tool to support design choices about parameters and eventually lead to better architecture designs. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.future.2017.07.033 | Future Generation Computer Systems |
Keywords | Field | DocType |
Modeling languages,Lambda architectural pattern,Performance evaluation,Multiformalism modeling,Multisolution methods,Cloud,Analytical approach | Data stream mining,Architecture,Data processing,COLA (software architecture),Computer science,Real-time computing,Resource allocation,Interference (wave propagation),Architectural pattern,Cloud computing,Distributed computing | Journal |
Volume | ISSN | Citations |
86 | 0167-739X | 4 |
PageRank | References | Authors |
0.50 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Gribaudo | 1 | 491 | 59.46 |
| Mauro Iacono | 2 | 263 | 27.47 |
| Mariam Kiran | 3 | 121 | 17.83 |