On the latency-accuracy tradeoff in approximate MapReduce jobs
To ensure the scalability of big data analytics, approximate MapReduce platforms emerge to explicitly trade off accuracy for latency. A key step to determine optimal approximation levels is to capture the latency of big data jobs, which is long deemed challenging due to the complex dependency among...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/22862
- Acceso en línea:
- https://doi.org/10.1109/INFOCOM.2017.8057038
https://repository.urosario.edu.co/handle/10336/22862
- Palabra clave:
- Approximation theory
Economic and social effects
Stochastic models
Stochastic systems
Data analytics
Data platform
Job scheduling policies
Map-reduce
Matrix analytic methods
Optimal approximation
Performance Gain
Wide spectrum
Big data
- Rights
- License
- http://purl.org/coar/access_right/c_abf2