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.
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.
Abstract:
An adaptive block cache management method and a DBMS applying the same are provided. A DB system according to an exemplary embodiment of the present disclosure includes: a cache configured to temporarily store DB data; a disk configured to permanently store the DB data; and a processor configured to determine whether to operate the cache according to a state of the DB system. Accordingly, a high-speed cache is adaptively managed according to a current state of a DBMS, such that a DB processing speed can be improved.
Abstract:
A cache management method for optimizing read performance in a distributed file system is provided. The cache management method includes: acquiring metadata of a file system; generating a list regarding data blocks based on the metadata; and pre-loading data blocks into a cache with reference to the list. Accordingly, read performance in analyzing big data in a Hadoop distributed file system environment can be optimized in comparison to a related-art method.