Abstract

Disclosed is a quota-less namespace resource management method and system for hyperparameter optimization in a Kubernetes environment. A resource management method performed by a resource management system may include performing resource management for searching for a hyperparameter combination of a machine learning model that achieves a target performance of the machine learning model in an available resource amount of Kubernetes; and searching for the hyperparameter combination of the machine learning model that achieves the target performance of the machine learning model according to the performed resource management.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 18/23211 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with adaptive number of clusters

Abstract

Disclosed is an efficient dynamic resource allocation method and system for maximizing a utilization in Kubernetes environment that may dynamically adjust a resource quota of a pod running in a Kubernetes cluster, and may step-wisely decrease a request and a limit of resources requested for the pod according to an adjustment rate if an actual resource usage of the pod is within a set range for a certain period of time, may increase a request and a limit for resources of the pod by a square of increased usage if the actual resource usage of the pod is out of the set range for the certain period of time, and may keep a portion of resources additionally secured by dynamically adjusting a resource quota of each of pods for resource increase of the existing pods.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

Abstract

Disclosed are a method and system for managing a hybrid Spark cluster for efficient Spark job execution. A cluster management method which is performed by a cluster management system may include, in a hybrid Spark cluster environment including a bare-metal-based cluster and a Kubernetes-based cluster, selecting a cluster manager comprising the bare-metal-based cluster or the Kubernetes-based cluster before a Spark client performs a Spark driver program, and performing a Spark job by using the selected cluster manager.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines

Goods & Services

Downloadable computer software for providing security to networks; Downloadable computer software for data processing; Downloadable computer software for database management; Downloadable computer operating programs; Downloadable computer software for use in data storage and management; Downloadable computer programs for use in telecommunications, namely, a virtual integrated solution to aggregate data from diverse data sources and repositories and to inquire relevant data through a single access channel; Downloadable computer programs for document management; database software applications, namely, downloadable computer applications for database maintenance and integration; Blank magnetic discs for storing computer programs and data

Abstract

The present invention relates to a system for storing and searching big data in real-time, wherein the system stores data in real-time in response to mass data generation without data loss in a memory, and allows the same to simultaneously be searched in real-time; and leaves only a predetermined amount of the data in a memory, stores the remaining old data in a Hadoop distributed file system (HDFS) in a structured format, and allows the same to be swiftly searched. The present invention realises the system for storing and searching big data in real-time, the system comprising: a data collection unit for collecting data through a data source library TeraStream BASS data source API (BDI); a client for retrieving data through a client library TeraStream BASS Client API (BCI); a data storage unit dualised with a Hadoop cluster that is a memory cluster and a disk storage space for collecting data in real-time; and a data retrieval and storage control unit for integrally managing clusters formed in a data storage unit, for managing data collection of the data collection unit, and for managing such that a retrieval result is transmitted via the web or a user interface (UI) according to a retrieval request from the client.

IPC Classes  ?

• G06F 17/30 - Information retrieval; Database structures therefor

Abstract

Provided are an apparatus and a method for replicating changed-data in a source database management system to a target database management system in real time, for minimizing the load on the source DBMS system, reducing costs for DBMS hardware configuration, and facilitating operation and failure recovery. The source server transmits the changed-data from a redo log file or an archive log file in a source DBMS area to a target server in real time, and the target server receives the changed-data transmitted from the source server to temporarily store the changed-data in a local area, and extracts the change data stored in the local area to replicate the extracted changed-data to the target DBMS, thereby implementing the apparatus for replicating the changed-data in the source database management system to the target database management system in real time.

IPC Classes  ?

• G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
• G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
• G06F 16/23 - Updating
• G06F 16/17 - Details of further file system functions
• G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements

Goods & Services

CD-ROM including data processing solutions; computer software, recorded.

Goods & Services

Computer software recorded on CD-ROMs for high speed and high performance data processing, high volume data extraction, data batching, data loading, data transformation and data migration and not for the purpose of integrating enterprise software applications