An ID3 Algorithm for Performance of Decision Tree in Predicting Student’s Absenteeism in an Academic Year using Categorical Datasets

Venkatesan, N.; Arasan, K. Arunmozhi Arasan Arunmozhi; Muthukumaran, S.

doi:10.17485/ijst/2015/v8i14/72730

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…In this work, we compare the performance of three cloud renderfarm services using two Multi Criteria Decision Making (MCDM) Algorithms namely the Analytical Hierarchical Processing (AHP) 11,12 and SAW (Simple Additive Weighting) 13 and draw useful insights. There are many works focusing on cloud services [14][15][16][17][18][19][20][21][22][23] and the ranking of Infrastructure-as-a-Service (IaaS) cloud services 24 . But very few works are aboutPaaS cloud renderfarm services 25 according to our literature survey.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multi Criteria Decision Making Algorithms for Ranking Cloud Renderfarm Services

Annette

Banu

Chandran³

2016

Indian Journal of Science and Technology

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Cloud services that provide a complete environment for the animators to render their files using the resources in the cloud are called Cloud Renderfarm Services. The objective of this work is to rank and compare the performance of these services using two popular Multi Criteria Decision Making (MCDM) Algorithms namely the Analytical Hierarchical Processing (AHP) and SAW (Simple Additive Weighting) methods. The performance of three real time cloud renderfarm services are ranked and compared based on five Quality of Service (QoS) attributes that are important to these services namely the Render Node Cost, File Upload Time, Availability, Elasticity and Service Response Time. The performance of these cloud renderfarm services are ranked in four different simulations by varying the weights assigned for each QoS attribute and the ranking obtained are compared. The results show that AHP and SAW assigned similar ranks to all three cloud renderfarm services for all simulations.

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