A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation

By G. Tesauro, Nicholas K. Jong, Rajarshi Das, Mohamed N. Bennani. International Conference on Automation and Computing, 2006

396 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/ee40e503717169171b2591a71e7dc6abdbe4b571

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In Computer Networks and Communications

In Information Systems

In Management Information Systems

Citations in the corpus

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “For example, neural networks [92, 99] take the state-action pairs as input and output the approximated Q-value.”
Autonomic multi-agent management of power and performance in data centers (2008, background): “One such approach is Hybrid Reinforcement Learning [18, 17], which has shown promising initial results in learning policies in high-dimensional state spaces for computational resource allocation, and for simultaneous management of performance and power consumption by dynamically modulating CPU clock”
Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments (2017, background): “Training offline can improve performance [Tesauro et al. 2006], but is directly affected by the quality of information provided.”
On the use of hybrid reinforcement learning for autonomic resource allocation (2007, background): “In very recent work [1–3, 8–11] a radically different approach based on Reinforcement Learning (RL) has been proposed for automatically learning management policies.”
Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems (2015, background): “Multi-Agent Learning (MAL) provides a promising paradigm f or studying how agents can learn coordinated behaviors in Mult i-Agent Systems (MASs) [1], and it is finding increasing application s i a wide range of real-world domains such as robotics [2], [3], d istributed control [4], [5], resource man”
A survey of autonomic computing—degrees, models, and applications (2008, methodology): “To this end, a number of hybrid models have been proposed which either speed up training or introduce domain knowledge to reduce the state space, for example, [Tesauro et al. 2006; Whiteson and Stone 2006].”
Elasticity in Cloud Computing: State of the Art and Research Challenges (2018, methodology): “[114] uses reinforcement learning and queuing theory.”