Cloud Instance Selection Using Parallel K-Means and AHP

Author:

Guo Taiyang1,Bahsoon Rami1,Chen Tao2,Elhabbash Abdessalam3,Samreen Faiza4,Elkhatib Yehia3

Affiliation:

1. University of Birmingham, Birmingham, United Kingdom

2. Loughborough University, Loughborough, United Kingdom

3. Lancaster University, Lancaster, United Kingdom

4. Lancaster Univresity, Lancaster, United Kingdom

Funder

Engineering and Physical Sciences Research Council

Publisher

ACM Press

Reference21 articles.

1. Akindele A. Bankole and Samuel A. Ajila. 2013. Predicting cloud resource provisioning using machine learning techniques. In Canadian Conference on Electrical and Computer Engineering (CCECE) . IEEE, 1--4. https://doi.org/10.1109/CCECE.2013.6567848

2. Alistair Baron and Paul Rayson. 2008. VARD2: A tool for dealing with spelling variation in historical corpora. In PG Conference in Corpus Linguistics .

3. Chia-Wei Chang, Pangfeng Liu, and Jan-Jan Wu. 2012. Probability-based cloud storage providers selection algorithms with maximum availability. In 41st International Conference on Parallel Processing. IEEE, 199--208.

4. Tao Chen and Rami Bahsoon. 2016. Self-adaptive and online qos modeling for cloud-based software services. IEEE Transactions on Software Engineering , Vol. 43, 5 (2016), 453--475.

5. Cloud Standards Coordination (Phase 2). 2016. Cloud Computing Users Needs - Analysis, conclusions and recommendations from a public survey . Special Report 003 381 V2.1.1. ETSI. 12--19 pages. http://csc.etsi.org/phase2/UserNeeds.html

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1. Performance Evaluation of Adaptive Neuro Fuzzy Inference System (ANFIS) for the Prediction of Cloud Service Provider;4th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing;2022-06-20

2. A2Cloud-H: A Multi-tiered Machine Learning Framework for Cost-Effective Cloud Resource Selection;Proceedings of the Future Technologies Conference (FTC) 2021, Volume 3;2021-10-25

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