MOP: Predicting Multiple Output in Multi-Sharing System
Dhanalakshmi B K1, Srikantaiah K C2, Venugopal K R3
1Dhanalakshmi B K, Department Computer science and Engineering, S J B Institute of Technology, Bangalore, India.
2Srikantaiah K C, Professor in the Department of Computer Science and Engineering at S J B Institute of Technology, Bangalore, India.
3Venugopal K R, Ph.D in Computer Science from Indian Institute of Technology, Madras.
Manuscript received on 11 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 4129-4137 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5482098319/2019©BEIESP | DOI: 10.35940/ijrte.C5482.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is relatively advanced field in which we believe resource utilization hasn’t yet been optimized to its complete potential and inaccuracy of prediction leads to several minutes of delays in instant resource allocation due to scarcity of resources in Multi-Sharing System. In this paper, we develop Extraction of Transaction Log Files to Predict Multiple Output (MOP) in Multi-Sharing System based on resource utilization for higher accuracy using prediction techniques Random Forest and majority voting algorithms. The goal is to gratify upcoming resource demands and to avoid over or under provisioning of resources. The accuracy results show that the proposed model provides higher accuracy in predicting resource utilization for upcoming resource demands and prediction cost and time are reduced.
Keywords: Cloud Computing , Log File, Majority Voting, Multiple-Output Prediction, Random Forest.
Scope of the Article: Cloud Computing