Applying Kolmogorov Complexity for High Load Balancing Between Distributed Computing System Nodes
Applying Kolmogorov Complexity for High Load Balancing Between Distributed Computing System Nodes

Summary/Abstract: Nowadays there is a huge growth of massive amount of data generated by different sources at real time or soft real time. Generally data is heterogeneous by its content and exists at every human sphere such as education, government, finance, medicine and so on. This paper is about the possibility to use the Kolmogorov complexity as a technological innovation in such area as education. The article describes fundamental issues such as data storage, data security and high speed access to data as one of the main parameters for successful using of eLearning platforms for education. It is considered comparisons of different eLearning platforms and its functional principles. Most eLearning platforms based on supercomputer and distributed computing systems, however, the growing volumes of data, especially in educational sphere, required other approaches of data storing and access. Nevertheless, educational platforms should be scalable and cross accessible. To increase data processing and storing efficiency it is considered data and operation partition idea among heterogeneous computational nodes which is the main infrastructure for eLearning platforms and methods. Kolmogorov complexity approach is investigated for such purposes. Furthermore, Kolmogorov complexity approach is examined for reduction a number of transmitting data between nodes. The possibility of using Kolmogorov complexity for optimal way of data dividing and processing in distributed and heterogeneous computing system and nodes without quantity nodes limitations is considered. The advantages and disadvantages of Kolmogorov complexity were considered applying to eLearning study aims. Experiments and results are given. The further plan investigation is described.

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