公开(公告)号：US10671445B2

公开(公告)日：2020-06-02

申请号：US15830490

申请日：2017-12-04

Applicant: Cisco Technology, Inc.

Inventor： Antonio Nucci ,  Dragan Milosavljevic ,  Ping Pamela Tang ,  Athena Wong ,  Alex V. Truong ,  Alexander Sasha Stojanovic ,  John Oberon ,  Prasad Potipireddi ,  Ahmed Khattab ,  Samudra Harapan Bekti

IPC: G06N5/02 ,  G06F9/50

Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.

公开(公告)号：US20190171494A1

公开(公告)日：2019-06-06

申请号：US15830490

申请日：2017-12-04

Applicant: Cisco Technology, Inc.

Inventor： Antonio Nucci ,  Dragan Milosavljevic ,  Ping Pamela Tang ,  Athena Wong ,  Alex V. Truong ,  Alexander Sasha Stojanovic ,  John Oberon ,  Prasad Potipireddi ,  Ahmed Khattab ,  Samudra Harapan Bekti

IPC: G06F9/50 ,  G06N5/02

Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.