Open AccessOptimization of request processing times for a heterogeneous data aggregation platform
Author(s) -
Victoria Tokareva
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1740/1/012058
Subject(s) - computer science , queue , distributed computing , scheduling (production processes) , flexibility (engineering) , fifo (computing and electronics) , data processing , load balancing (electrical power) , data aggregator , real time computing , database , computer network , mathematical optimization , operating system , statistics , geometry , mathematics , wireless sensor network , grid
A heterogeneous data aggregation system, e.g. developed within the frame of the GRADLC project, allows for a flexible expansion by connecting new data storages, as well as providing researchers a fast and aggregated access to heterogeneous data from independent (astroparticle physics) projects, while reducing the load on the original data storages. However, this flexibility requires balancing user requests in the queue with respect to various request processing times for the distributed storages, taking into account the different data processing policies on each particular storage. In order to attack this problem, a mathematical model of the data aggregation system was developed, and approaches to optimization of the request ordering in the processing queue are proposed and investigated by performing a numerical experiment. Based on this results, a job shop scheduling algorithm was revealed which gives benefit in mean request processing times compared to the well-known first in, first out (FIFO) model.