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Abstract
The paper presents a new classification schema of IaaS cloud workloads types, based on the functional characteristics. We show the results of an experiment of automatic categorization performed with different benchmarks that represent particular workload types. Monitoring of resource utilization allowed us to construct workload models that can be processed with machine learning algorithms. The direct connection between the functional classes and the resource utilization was shown, using unsupervised categorization approach based on moving average for finding a class number, and k-means algorithm for clustering.
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Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- Proceedings of the International Conference on Signal, Networks, Computing, and Systems. - Vol. 1 strony 303 - 313
- ISSN:
- 1876-1100
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Orzechowski P., Proficz J., Krawczyk H., Szymański J.: Categorization of Cloud Workload Types with Clustering// / : Springer India, 2017, s.303-313
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-81-322-3592-7_31
- Verified by:
- Gdańsk University of Technology
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