Users' behaviours show a noticeable impact on cloud computing resources. Behaviour prediction models could foster usage awareness of cloud users. This requires training prediction models with datasets that provide...
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ISBN:
(纸本)9781665469586
Users' behaviours show a noticeable impact on cloud computing resources. Behaviour prediction models could foster usage awareness of cloud users. This requires training prediction models with datasets that provide user information. Unfortunately, such information is excluded from many relevant datasets. Therefore, in this work, we investigate the ability of extracting these identities via clustering methods. We conduct this by categorising workload datasets according to the availability of users information in their attributes. Then, we focus our attention on shared attributes between user information disclosing and non-disclosing datasets. Eventually, we evaluated the potential of several clustering approaches on user information disclosing datasets. Our results show that users' identifications can be extracted with relatively high accuracy using clustering. They also show that the highest clustering precision is mostly obtained from the attributes representing request components that strongly relate to the user's application.
The data of this research describes the logged usage of the Euler cluster located at CIEMAT (Centre for Energy, Environment, and Technology Research), spanning the period between November 2008 and March 2018. The Eule...
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The data of this research describes the logged usage of the Euler cluster located at CIEMAT (Centre for Energy, Environment, and Technology Research), spanning the period between November 2008 and March 2018. The Euler database is open access in parallel workload archive format, available from the PWA repository [1] and Mendeley Data [2], allowing in this way a whole new bunch of research possibilities on computer science. (C) 2019 The Author(s). Published by Elsevier Inc.
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