Analysis on Improving the Response Time with PIDSARSA-RAL in ClowdFlows Mining Platform

Journal Title: EAI Endorsed Transactions on Energy Web - Year 2018, Vol 5, Issue 20

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.

Authors and Affiliations

N. Yuvaraj, R. Arshath Raja, Dr. V. Ganesan, Dr. C. Suresh Gnana Dhas

Keywords

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  • EP ID EP45380
  • DOI http://dx.doi.org/10.4108/eai.12-9-2018.155557
  • Views 172
  • Downloads 0

How To Cite

N. Yuvaraj, R. Arshath Raja, Dr. V. Ganesan, Dr. C. Suresh Gnana Dhas (2018). Analysis on Improving the Response Time with PIDSARSA-RAL in ClowdFlows Mining Platform. EAI Endorsed Transactions on Energy Web, 5(20), -. https://europub.co.uk/articles/-A-45380