公开(公告)号：US12088665B2

公开(公告)日：2024-09-10

申请号：US17497183

申请日：2021-10-08

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Young Hwan Kim

IPC: H04L67/1095 ,  H04L41/12 ,  H04L67/12

CPC classification number: H04L67/1095 ,  H04L41/12 ,  H04L67/12

Abstract: There is provided a method of data replication between management modules in a rugged environment. According to an embodiment of the present disclosure, an edge server management module replication method includes: a step of collecting, by a first management module, environment information of an edge server; a step of managing, by the first management module, the edge server, based on the collected environmental information; a first storage step of storing, by the first management module, management data related to the edge server in a repository of the first management module; and a second storage step of storing, by a second management module, the management data stored at the first storage step in a repository of the second management module. Accordingly, the time required to respond to an error occurring in a severe industrial site can be minimized through data replication processing between replicated edge management modules in a rugged environment, so that a continuous edge service can be provided without interruption.

公开(公告)号：US09864520B2

公开(公告)日：2018-01-09

申请号：US15008400

申请日：2016-01-27

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Chang Won Park ,  Young Hwan Kim

IPC: G06F3/06 ,  G06F13/18

CPC classification number: G06F3/0605 ,  G06F3/0631 ,  G06F3/067 ,  G06F3/0685 ,  G06F13/18

Abstract: A policy-based orchestration method in an exascale class cloud storage environment, and a storage system using the same are provided. The storage orchestration method includes: allocating a combination of different storages to a user as a storage space; and adjusting the combination according to a user's using pattern. Accordingly, the storage can be operated optimally and autonomically, and thus can be operated efficiently and economically.

公开(公告)号：US10805176B2

公开(公告)日：2020-10-13

申请号：US16147945

申请日：2018-10-01

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Young Hwan Kim ,  Seung Gwon Kim

IPC: G06F17/20 ,  H04L12/24 ,  G06F8/65 ,  G06F3/0482

Abstract: A method for generating firmware by allowing a developer to freely select functions to be included in firmware installed on a main board of a server, and by building a firmware image is provided. The method for generating firmware includes: listing functions that are allowed to be included in firmware installed on a main board of a server; receiving selection of at least one of the listed functions from a user; and building a firmware image including the functions selected by the user.Accordingly, since a firmware image is built by a developer freely selecting functions to be included in firmware installed on a main board of a server, firmware optimized for requirements of the developer can be generated.

公开(公告)号：US12099884B2

公开(公告)日：2024-09-24

申请号：US17467963

申请日：2021-09-07

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Young Hwan Kim

IPC: G06F9/50 ,  G06F9/38

CPC classification number: G06F9/5077 ,  G06F9/3891 ,  G06F9/5005

Abstract: There are provided a cloud management method and a cloud management apparatus for rapidly scheduling arrangements of service resources by considering equal distribution of resources in a large-scale container environment of a distributed collaboration type. The cloud management method according to an embodiment includes: receiving, by a cloud management apparatus, a resource allocation request for a specific service; monitoring, by the cloud management apparatus, available resource current statuses of a plurality of clusters, and selecting a cluster that is able to be allocated a requested resource; calculating, by the cloud management apparatus, a suitable score with respect to each of the selected clusters; and selecting, by the cloud management apparatus, a cluster that is most suitable to the requested resource for executing a requested service from among the selected clusters, based on the respective suitable scores. Accordingly, for the method for determining equal resource arrangements between associative clusters according to characteristics of a required resource, a model for selecting a candidate group and finally selecting a cluster that is suitable to a required resource can be supported.

公开(公告)号：US11575740B2

公开(公告)日：2023-02-07

申请号：US17467804

申请日：2021-09-07

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Young Hwan Kim

IPC: G06F15/173 ,  H04L67/1021 ,  G06F9/50 ,  H04L67/1008

Abstract: There is provided a cloud management method and apparatus for performing load balancing so as to make a service in a cluster that is geographically close in an associative container environment and has a good resource current status. The cloud management method according to an embodiment includes: monitoring, by a cloud management apparatus, available resource current statuses of a plurality of clusters, and selecting a cluster that owns a first service supported by a first cluster an available resource rate of which is less than a threshold value; calculating, by the cloud management apparatus, scores regarding an available resource current status and geographical proximity of each cluster; and performing, by the cloud management apparatus, load balancing of the first service, based on a result of calculating the scores. Accordingly, a delay in a response speed of a service that is required in a distributed environment can be minimized, and a service can be supported to be processed in a geographically close cluster through analysis of geographical closeness (proximity) between an access location where there is a user request and a cluster in which services are distributed.

公开(公告)号：US11429449B2

公开(公告)日：2022-08-30

申请号：US17082798

申请日：2020-10-28

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Young Hwan Kim

IPC: G06F9/50 ,  G06F11/34

Abstract: A cloud management method and a cloud management device are provided. The cloud management method receives a resource allocation request for a specific service, calculates an idle resource current state score regarding each resource of each node of each cluster by monitoring virtual resource usage current states of a plurality of nodes included in a plurality of clusters, and determines a node to allocate resources for executing the requested specific service, based on the calculated idle resource current state score. Accordingly, a score is given to a resource current state of an associated cluster, and a resource candidate group is selected in response to a service scheduling request, based on the score, and an optimal node is selected based on required resources necessary for the service.

公开(公告)号：US12028269B2

公开(公告)日：2024-07-02

申请号：US17983770

申请日：2022-11-09

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Young Hwan Kim

IPC: H04L47/783

CPC classification number: H04L47/783

Abstract: There are provided a method and an apparatus for cloud management, which selects optimal resources based on graphic processing unit (GPU) resource analysis in a large-scale container platform environment. According to an embodiment, a GPU bottleneck phenomenon occurring in an application of a large-scale container environment may be reduced by processing partitioned allocation of GPU resources, rather than existing 1:1 allocation, through real-time GPU data analysis (application of a threshold) and synthetic analysis of GPU performance degrading factors.

公开(公告)号：US11314559B2

公开(公告)日：2022-04-26

申请号：US17082446

申请日：2020-10-28

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Young Hwan Kim

IPC: G06F9/50

Abstract: A cloud management method and a cloud management device are provided. The cloud management method determines whether a plurality of pods are overloaded, identifies resource usage current states of a cluster and a node, and determines a method of scaling resources of a specific pod that is overloaded from among the plurality of pods, according to the resource usage current states of the cluster and the node, and scales the resources of the specific pod according to the determined method. Accordingly, scaling for uniformly extending resources of a node and a pod in a cluster horizontally and vertically can be automatically performed.

公开(公告)号：US11093291B2

公开(公告)日：2021-08-17

申请号：US15895242

申请日：2018-02-13

Applicant: KOREA ELECTRONICS TECHNOLOGY INSTITUTE

Inventor： Jae Hoon An ,  Jae Gi Son ,  Ji Woo Kang

IPC: G06F9/50

Abstract: A resource assignment method, and a recording medium and a distributed processing device applying the same are provided. The resource assignment method includes: when information regarding a plurality of tasks is received from a plurality of first nodes, calculating a size of a resource necessary for each of the received plurality of tasks; and when information regarding an available resource is received from a second node, assigning one of the plurality of tasks to the available resource of the second node, based on the calculated size of the resource necessary for each task.

公开(公告)号：US10671636B2

公开(公告)日：2020-06-02

申请号：US15464798

申请日：2017-03-21

Applicant: Korea Electronics Technology Institute

Inventor： Jae Hoon An ,  Jae Gi Son ,  Chang Won Park

IPC: G06F15/16 ,  G06F16/27

Abstract: Provided herein is an In-Memory DB connection support type scheduling method and system for real-time big data analysis in distributed computing environment. The data processing method according to an embodiment of the present disclosure analyzes data based on a distributed computing environment using a distributed system and dynamically alters a structure of a distributed DB constituting the distributed system based on the distributed computing environment. By this method, it is possible to secure concurrency adaptively to the distributed computing environment by dynamically managing the number of shards, and secure real-timeliness through TMO-based scheduling, thereby ultimately improving the speed/efficiency of big data analysis.