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Title:

Ismael: Using Machine Learning To Predict Acceptance of Virtual Clusters in Data Centers

Document type:
Zeitschriftenaufsatz
Author(s):
Zerwas, Johannes; Kalmbach, Patrick; Schmid, Stefan; Blenk, Andreas
Abstract:
Existing virtual network admission control algorithms targeting high utilization of data center infrastructure are computationally expensive or provide poor performance. In particular, existing algorithms have in common that they are oblivious to the past, i.e., requests are handled in a fire-and-forget manner, not taking into account information from previously solved instances. This can be inefficient and misses out on a basic optimization opportunity: as for any network optimization algorithm...     »
Keywords:
Data Center Resource Management , Virtual Cluster , Admission Control , Machine Learning
Horizon 2020:
647158
Journal title:
IEEE Transactions on Network and Service Management
Year:
2019
Fulltext / DOI:
doi:10.1109/TNSM.2019.2927291
Semester:
SS 19
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