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CloubrAIn careers
CloubrAIn defragments the placement of your datacenter workloads.
Workloads placed manually lead to wasted resources, QoS violations, and frustration. Current processes for deploying and managing workloads are not capable of operating at the necessary scale, and cannot model the dependencies and global constraints of workloads. These decisions are error prone because they are based on a narrow view of the datacenter.
Cloubrain leverages big data and machine learning to defragment the placement of your workloads. Our technique is like optimally playing multidimensional Tetris. The end result assures datacenter performance and efficiency, while preventing problems from occurring. This enables engineers to focus on building their business, and not on managing their cloud infrastructure.
Workloads placed manually lead to wasted resources, QoS violations, and frustration. Current processes for deploying and managing workloads are not capable of operating at the necessary scale, and cannot model the dependencies and global constraints of workloads. These decisions are error prone because they are based on a narrow view of the datacenter.
Cloubrain leverages big data and machine learning to defragment the placement of your workloads. Our technique is like optimally playing multidimensional Tetris. The end result assures datacenter performance and efficiency, while preventing problems from occurring. This enables engineers to focus on building their business, and not on managing their cloud infrastructure.
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Pete Perlegos
BS, MS in EECS from UC Berkeley
JD focused on Business Law, Securities Regulation
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