Research Article
Analysis on Improving the Response Time with PIDSARSA-RAL in ClowdFlows Mining Platform
@ARTICLE{10.4108/eai.12-9-2018.155557, author={N. Yuvaraj and R. Arshath Raja and Dr. V. Ganesan and Dr. C. Suresh Gnana Dhas}, title={Analysis on Improving the Response Time with PIDSARSA-RAL in ClowdFlows Mining Platform}, journal={EAI Endorsed Transactions on Energy Web and Information Technologies}, volume={5}, number={20}, publisher={EAI}, journal_a={EW}, year={2018}, month={9}, keywords={SARSA Active Learning, Big Data Mining, PID Controller, Reinforcement Learning}, doi={10.4108/eai.12-9-2018.155557} }
- N. Yuvaraj
R. Arshath Raja
Dr. V. Ganesan
Dr. C. Suresh Gnana Dhas
Year: 2018
Analysis on Improving the Response Time with PIDSARSA-RAL in ClowdFlows Mining Platform
EW
EAI
DOI: 10.4108/eai.12-9-2018.155557
Abstract
This paper provides an improved parallel data processing in Big Data mining using ClowdFlows platform. The big data processing involves an improvement in Proportional Integral Derivative (PID) controller using Reinforcement Adaptive Learning (RAL). The Reinforcement Adaptive Learning involves the use of Actor-critic State–action–reward–state–action (SARSA) learning that suits well the stream mining module of ClowdFlows platform. The study concentrates on batch mode processing in Big Data mining model with the use of proposed PID-SARSA-RAL. The experimental evaluation with the conventional ClowdFlows platform proved the effectiveness of the proposed method over continuous parallel workflow execution.
Copyright © 2018 N. Yuvaraj et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.