Abstract: In the modern information society, the volume of data is constantly growing, and its effective processing is becoming key for enterprises. The transmission and storage of this data also plays a critical role. Big data used in analytics systems is most often transmitted in one of two popular formats: CSV for structured data and JSON for unstructured data. However, existing file formats may not be effective or flexible enough for certain data analysis tasks. For example, they may not support complex data structures or provide sufficient control over metadata. Alternatively, analytical tasks may require additional information about the data, such as metadata, data schema, etc. Based on the above, the subject of this study is a data format based on the combined use of CSV and JSON for processing and analyzing large amounts of information. The option of sharing the designated data types for the implementation of a new data format is proposed. For this purpose, designations have been introduced for the data structure, which includes CSV files, JSON files, metadata and a dependency graph. Various types of functions are described, such as aggregating, transforming, filtering, etc. Examples of the application of these functions to data are given. The proposed approach is a technique that can significantly facilitate the processes of information analysis and processing. It is based on a formalized approach that allows you to establish clear rules and procedures for working with data, which contributes to their more efficient processing. Another aspect of the proposed approach is to determine the criteria for choosing the most appropriate data storage format. This criterion is based on the mathematical principles of information theory and entropy. The introduction of a criterion for choosing a data format based on entropy makes it possible to evaluate the information content and compactness of the data. This approach is based on the calculation of entropy for selected formats and weights reflecting the importance of each data value. By comparing entropies, you can determine the required data transmission format. This approach takes into account not only the compactness of the data, but also the context of their use, as well as the possibility of including additional meta-information in the files themselves and supporting data ready for analysis.

Abstract: The purpose of this article is to consider the correlation of the performance of computational load balancing algorithms for globally distributed computing systems implementing the principle of volunteer computing and the basic attributes of a distributed system. As the main considered parameters the author examines file system structure and the type of network protocol. The object of the study is a globally distributed computing system with scheduling nodes loading. The subject of the study are the balancing methods of loading units of the system, implementing the principle of dynamic computational load balancing strategy. In this article, the methodological basis of the article makes methods of fundamental and applied sciences: analysis methods, methods of mathematical statistics, simulation modeling. The author suggests a model of node computational load in form of nonlinear piecewise-stationary model. The paper shows a method of computing experiment to determine the effectiveness of the balancing algorithms. The author develops a simulation model of distributed complex with the possibility of setting the basic system parameters computer system  and assess their impact on system response time. It is shown that a particular impact on the efficiency of load balancing algorithms have such parameters as file system structure and the type of network protocol. Thus, the necessity of taking into account these parameters to ensure the adequacy of the developed model of the distributed computing system implemented on the basis of volunteer computing.