Abstract: Disclosed herein is a method for executing Artificial Intelligence (AI) services based on virtual infrastructures. The method includes configuring the sharing type of a computational processing unit, executing an AI service based on a virtual infrastructure using requirements for the AI service and information about the sharing type of the computational processing unit, and performing optimization for the AI service.

Abstract: Disclosed herein is an apparatus, method, and storage medium for integrated management of virtualization of computer resources. The apparatus receives a user request from a user; classifies the received user request depending on virtualization models for the one or more nodes, provides the classified user request to at least one interface of the virtualization models and performs an integration manager of the virtualization models.

Abstract: Disclosed herein are an intelligent scheduling apparatus and method. The intelligent scheduling apparatus includes one or more processors, and an execution memory for storing at least program that is executed by the one or more processors, wherein the at least one program is configured to, in a hybrid cloud environment including a cloud, an edge system, and a near-edge system, configure schedulers for scheduling tasks of the cloud, the edge system, and the near-edge systems, store data, requested by a client, in a work queue by controlling the schedulers based on a scheduler policy and process the tasks based on data stored in the work queue, and collect history data resulting from processing of the tasks depending on the scheduler policy, and train the scheduler policy based on the history data.

Abstract: Disclosed herein are an apparatus and method for managing in-memory container storage. The apparatus includes one or more processors, executable memory for storing at least one program executed by the one or more processors, and a container file system for storing a container, which provides application virtualization. Here, the container file system includes a merged access layer, a container layer, and an image layer, and the at least one program provides an application with link information of files in the container layer and the image layer, thereby allowing the application to access the files.

Abstract: Disclosed herein is a method for resource allocation in an edge-computing environment. The method includes receiving a request for an intelligent edge service, selecting the worker server to execute the service based on an input/output congestion level, allocating resources based on topology information of the worker server, and configuring a virtual environment based on the allocated resources.

Abstract: Disclosed herein are a distributed cloud system, a data processing method of a distributed cloud system, and a storage medium. The data processing method of a distributed cloud system includes receiving a request of a user for an edge cloud and controlling a distributed cloud system, wherein the distributed cloud system comprises a core cloud including a large-scale resource, the edge cloud, and a local cloud including a middle-scale resource between the core cloud and the edge cloud, processing tasks corresponding to the user request through a scheduler of the core cloud, distributing the tasks based on a queue, and aggregating results of processed tasks, and providing processed data in response to a request of the user, wherein the distributed cloud system provides a management function in case of failure in the distributed cloud system.

Abstract: Disclosed herein are an apparatus and method for managing memory-based integrated storage. The apparatus includes one or more processors and executable memory for storing at least one program executed by the one or more processors. The at least one program converts data operation tasks in response to a request for access to memory-based integrated storage from a user, a single virtual disk of a virtual storage pool of the memory-based integrated storage converts a disk access command into a command for connecting to a storage backend depending on the data operation tasks, and conversion of the data operation tasks into the command includes target identification indicating which local storage of the memory-based integrated storage is to be used.

Abstract: Disclosed herein are a distributed cloud system and a data-processing method and storage medium of the distributed cloud system. The data-processing method of the distributed cloud system includes executing an application of an edge computer system, requested by a user device, creating a snapshot image of the application, and storing the created image and transferring the stored image when migration is performed.

Abstract: Disclosed herein are an apparatus and method for autoscaling a service shared in a cloud. The apparatus may include memory in which at least one program is recorded and a processor for executing the program, and the program may perform autoscaling by which at least one second service for performing the same function as a first service is additionally generated or deleted depending on a load that is incurred when multiple clients call the first service in the cloud. The at least one second service may be set to one of two or more execution types having different response times depending on a response time required by each of the multiple clients.

Abstract: Disclosed herein are an apparatus and method for managing in-memory container storage. The apparatus includes one or more processors, executable memory for storing at least one program executed by the one or more processors, and a container file system for storing a container, which provides application virtualization. Here, the container file system includes a merged access layer, a container layer, and an image layer, and the at least one program provides an application with link information of files in the container layer and the image layer, thereby allowing the application to access the files.

Abstract: Disclosed herein are a Docker image creation apparatus and method. The Docker image creation apparatus may include one or more processors, and execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to create a Docker image based on a previous Docker file, and executing a push command to store the created the Docker file to a registry, wherein the Docker file is created from a partial Docker file of the previous Docker file, wherein the Docker image is created from a verified partial Docker file, after the partial Docker file is verified.

Abstract: Disclosed herein are a distributed cloud system, a data processing method of the distributed cloud system, a storage medium. The data processing method of the distributed cloud system includes running an application of an edge computing system requested by a user device, generating a snapshot image of the application, and storing the generated snapshot image and transmitting the stored image during migration.

Abstract: Disclosed herein are an intelligent scheduling apparatus and method. The intelligent scheduling apparatus includes one or more processors, and an execution memory for storing at least program that is executed by the one or more processors, wherein the at least one program is configured to, in a hybrid cloud environment including a cloud, an edge system, and a near-edge system, configure schedulers for scheduling tasks of the cloud, the edge system, and the near-edge systems, store data, requested by a client, in a work queue by controlling the schedulers based on a scheduler policy and process the tasks based on data stored in the work queue, and collect history data resulting from processing of the tasks depending on the scheduler policy, and train the scheduler policy based on the history data.

Abstract: Disclosed herein is a multi-cloud edge system. The multi-cloud edge system includes a core cloud, a multi-cluster-based first edge node system, and a multi-cluster-based near edge node system, wherein the multi-cluster-based first edge node system includes multiple worker nodes, and a master node including a scheduler.

Abstract: Disclosed herein are a distributed cloud system, a data processing method of a distributed cloud system, and a storage medium. The data processing method of a distributed cloud system includes receiving a request of a user for an edge cloud and controlling a distributed cloud system, wherein the distributed cloud system comprises a core cloud including a large-scale resource, the edge cloud, and a local cloud including a middle-scale resource between the core cloud and the edge cloud, processing tasks corresponding to the user request through a scheduler of the core cloud, distributing the tasks based on a queue, and aggregating results of processed tasks, and providing processed data in response to a request of the user, wherein the distributed cloud system provides a management function in case of failure in the distributed cloud system.

Abstract: Disclosed herein are an intelligent scheduling apparatus and method. The intelligent scheduling apparatus includes one or more processors, and an execution memory for storing at least program that is executed by the one or more processors, wherein the at least one program is configured to, in a hybrid cloud environment including a cloud, an edge system, and a near-edge system, configure schedulers for scheduling tasks of the cloud, the edge system, and the near-edge systems, store data, requested by a client, in a work queue by controlling the schedulers based on a scheduler policy and process the tasks based on data stored in the work queue, and collect history data resulting from processing of the tasks depending on the scheduler policy, and train the scheduler policy based on the history data.

Abstract: Disclosed herein is a method for resource allocation in an edge-computing environment. The method includes receiving a request for an intelligent edge service, selecting the worker server to execute the service based on an input/output congestion level, allocating resources based on topology information of the worker server, and configuring a virtual environment based on the allocated resources.

Abstract: Disclosed herein is a multi-cloud edge system. The multi-cloud edge system includes a core cloud, a multi-cluster-based first edge node system, and a multi-cluster-based near edge node system, wherein the multi-cluster-based first edge node system includes multiple worker nodes, and a master node including a scheduler.

Abstract: Disclosed herein are a Docker image creation apparatus and method. The Docker image creation apparatus may include one or more processors, and execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to create a Docker image based on a previous Docker file, and executing a push command to store the created the Docker file to a registry, wherein the Docker file is created from a partial Docker file of the previous Docker file, wherein the Docker image is created from a verified partial Docker file, after the partial Docker file is verified.

Abstract: Disclosed herein are an apparatus and method for managing in-memory container storage. The apparatus includes one or more processors, executable memory for storing at least one program executed by the one or more processors, and a container file system for storing a container, which provides application virtualization. Here, the container file system includes a merged access layer, a container layer, and an image layer, and the at least one program provides an application with link information of files in the container layer and the image layer, thereby allowing the application to access the files.