On the use of hybrid reinforcement learning for autonomic resource allocation

@article{Tesauro2007OnTU,
  title={On the use of hybrid reinforcement learning for autonomic resource allocation},
  author={Gerald Tesauro and Nicholas K. Jong and Rajarshi Das and Mohamed N. Bennani},
  journal={Cluster Computing},
  year={2007},
  volume={10},
  pages={287-299}
}
Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of explicit system performance models. In principle, RL can automatically learn high-quality management policies without an explicit performance model or traffic model, and with little or no built-in system specific knowledge. In our original work (Das, R., Tesauro, G., Walsh, W.E.: IBM Research, Tech. Rep. RC23802 (2005… CONTINUE READING
Highly Cited
This paper has 130 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 83 extracted citations

131 Citations

01020'09'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 131 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 32 references

Model-based and modelfree approaches to autonomic resource allocation

  • R. Das, G. Tesauro, W. E. Walsh
  • IBM Research, Tech. Rep. RC23802
  • 2005
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…