公开(公告)号：US20220164186A1

公开(公告)日：2022-05-26

申请号：US17103156

申请日：2020-11-24

Applicant: International Business Machines Corporation

Inventor： Sreenivasa Rao Pamidala ,  Jayachandu Bandlamudi ,  Gandhi Sivakumar ,  Ernese Norelus

IPC: G06F9/22 ,  G06F9/50 ,  G06K9/62 ,  G06F9/54

Abstract: Performing container scaling and migration for container-based microservices is provided. A first set of features is extracted from each respective microservice of a plurality of different microservices. A number of containers required at a future point in time for each respective microservice of the plurality of different microservices is predicted using a trained forecasting model and the first set of features extracted from each respective microservice. A scaling label and a scaling value are assigned to each respective microservice of the plurality of different microservices based on a predicted change in a current number of containers corresponding to each respective microservice according to the number of containers required at the future point in time for each respective microservice. The current number of containers corresponding to each respective microservice of the plurality of different microservices is adjusted based on the scaling label and the scaling value assigned to each respective microservice.

公开(公告)号：US11704123B2

公开(公告)日：2023-07-18

申请号：US17103156

申请日：2020-11-24

Applicant: International Business Machines Corporation

Inventor： Sreenivasa Rao Pamidala ,  Jayachandu Bandlamudi ,  Gandhi Sivakumar ,  Ernese Norelus

IPC: G06F9/50 ,  G06F9/54 ,  G06F9/22 ,  G06F18/213 ,  G06F18/214

CPC classification number: G06F9/22 ,  G06F9/5072 ,  G06F9/5077 ,  G06F9/541 ,  G06F18/213 ,  G06F18/2155

Abstract: Performing container scaling and migration for container-based microservices is provided. A first set of features is extracted from each respective microservice of a plurality of different microservices. A number of containers required at a future point in time for each respective microservice of the plurality of different microservices is predicted using a trained forecasting model and the first set of features extracted from each respective microservice. A scaling label and a scaling value are assigned to each respective microservice of the plurality of different microservices based on a predicted change in a current number of containers corresponding to each respective microservice according to the number of containers required at the future point in time for each respective microservice. The current number of containers corresponding to each respective microservice of the plurality of different microservices is adjusted based on the scaling label and the scaling value assigned to each respective microservice.