Corpus ID: 218537957

Boosting Cloud Data Analytics using Multi-Objective Optimization

@article{Song2020BoostingCD,
  title={Boosting Cloud Data Analytics using Multi-Objective Optimization},
  author={Fei Song and Khaled Zaouk and Chenghao Lyu and Arnab Sinha and Qi Fan and Y. Diao and P. Shenoy},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.03314}
}
Data analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters… Expand

References

SHOWING 1-10 OF 48 REFERENCES
MRTuner: A Toolkit to Enable Holistic Optimization for MapReduce Jobs
  • 56
  • PDF
Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics
  • 281
  • PDF
Supporting Scalable Analytics with Latency Constraints
  • 29
  • PDF
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases
  • 34
  • PDF
Schedule optimization for data processing flows on the cloud
  • 145
  • Highly Influential
  • PDF
PerfOrator: eloquent performance models for Resource Optimization
  • 35
  • Highly Influential
  • PDF
A Learning-Based Service for Cost and Performance Management of Cloud Databases
  • 6
  • PDF
Tempo: Robust and Self-Tuning Resource Management in Multi-tenant Parallel Databases
  • 18
  • PDF
Towards a Learning Optimizer for Shared Clouds
  • 39
  • PDF
...
1
2
3
4
5
...