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Document type:
Zeitschriftenaufsatz 
Author(s):
Zerwas, Johannes; Kalmbach, Patrick; Schmid, Stefan; Blenk, Andreas 
Title:
Ismael: Using Machine Learning To Predict Acceptance of Virtual Clusters in Data Centers 
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 
Semester:
SS 19