The procedural generation of planetoids finds its place in the field of visualization of virtual worlds in video games, movies, and simulation tools. Due to the growing popularity of the application, the requirements for the quality, uniqueness, and scalability of visualization results are increasing, which, in turn, leads to higher requirements for hardware computing resources. This paper proposes a solution for the architecture of a software system for generating planetoid landscapes, based on a combination of a distributed computing system and the use of parallelism based on the Morsel-Driven Query Execution algorithm to overcome hardware limitations. The computing model includes the following components: a main server that supports gRPC connections; worker servers that perform the task of generating planetoid landscapes in parallel; a geospatial database containing vector data of generated planetoids (rivers and reservoirs; geographic regions – biomes, forests, and other road segments) binary storage of three-dimensional models that are superimposed on the generated planetoid landscape; tileset-storage for storing raster data required for a generation; users who use the software system for generating planetoid landscapes to realize their own goals. The use of software agents in the built system allows unifying a set of algorithms as a single entity used for a particular stage of landscape generation and solving the problem of software system extensibility. A distributed messaging system, a broker, is used to send and process requests using a topic per software agent sequence position. The broker utilizes load balancing to deliver landscape generation requests to background workers. To analyze the system’s performance, experiments were conducted with different numbers of background workers (1, 2: 4, 8; 16) and segment sizes of 512, and 2048 pixels. The lowest average time for generating one segment was obtained when the segment size was 512 pixels and the number of segments was 64. The average segment generation time for the above experiments ranged from 0.33 to 9.32 seconds. The integrated architectural solution allowed to reduce the CPU time by 2 to 5 times compared to a system that uses the approach of storing queries in the database. The solution’s efficiency is especially noticeable with large amounts of data, which is determined by the number of segments and their size in pixels.
[1] Cassol, V. J., Marson, F. P., Vendramini, M., Paravisi, M., Bicho, A. L., Jung, C. R., & Musse, S. R. (2011). Simulation of autonomous agents using terrain reasoning. In Proc. the Twelfth IASTED International Conference on Computer Graphics and Imaging (CGIM 2011), Innsbruck, Austria. IASTED/ACTA Press. http://dx.doi.org/10.2316/P.2011.722-017
[2] Hyttinen, T., Mäkinen, E., & Poranen, T. (2017). Terrain synthesis using noise by examples. In Proceedings of the 21st International Academic Mindtrek Conference. (pp. 17–25). https://doi.org/10.1145/3131085.3131099
[3] Kamal, K. R., & Uddin, Y. S. (2007). Parametrically controlled terrain generation. In Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia. (pp. 17–23). https://doi.org/10.1145/1321261.1321264
[4] Henham, W., Holloway, D., & Panton, L. (2016). Broadband acoustic scattering with modern aesthetics from random 3D terrain surfaces generated using the Fourier Synthesis algorithm. Acoustics 2016: The Second Australasian Acoustical Societies Conference. (pp. 203–212).
[5] Spick, R. J., Cowling, P., & Walker, J. A. (2019). Procedural generation using spatial GANs for region-specific learning of elevation data. 2019 IEEE Conference on Games (CoG), (pp. 1–8). IEEE. https://doi.org/10.1109/CIG.2019.8848120
[6] Castle, C. J., & Crooks, A. T. (2006). Principles and concepts of agent-based modelling for developing geospatial simulations.
[7] Hofmann, P., Andrejchenko, V., Lettmayer, P., Schmitzberger, M., Gruber, M., Ozan, I., & Blaschke, T. (2016). Agent based image analysis (ABIA)-preliminary research results from an implemented framework. https://doi.org/10.3990/2.455
[8] Jahanbani, M., Vahidnia, M. H., & Aspanani, M. (2022). Geographical agent-based modeling and satellite image processing with application to facilitate the exploration of minerals in Behshahr. Iran. Arabian Journal of Geosciences, 15(9), 901. https://doi.org/10.1007/s12517-022-10165-8
[9] Ali, S. M., Doolan, M., Wernick, P., & Wakelam, E. (2018). Developing an agent-based simulation model of software evolution. Information and Software Technology, 96, 126–140. https://doi.org/10.1016/j.infsof.2017.11.013
[10] Aderum, O., & Åkerlund, J. (2016). Controllable Procedural Game Map Generation using Software Agents and Mixed Initiative.
[11] Roohitavaf, M., Ren, K., Zhang, G., & Ben-Romdhane, S. (2019). LogPlayer: Fault-tolerant Exactly-once Delivery using gRPC Asynchronous Streaming. https://doi.org/10.48550/arXiv.1911.11286
[12] Krämer, M., & Senner, I. (2015). A modular software architecture for processing of big geospatial data in the cloud. Computers & Graphics, 49, 69–81. https://doi.org/10.1016/j.cag.2015.02.005
[13] Leis, V., Boncz, P., Kemper, A., & Neumann, T. (2014). Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age. Proceedings of the 2014 ACM SIGMOD international conference on Management of data, (pp. 743–754). https://doi.org/10.1145/2588555.2610507
[14] Jacobsen, R. H., Jeppesen, J. H., Laursen, K. F., Skovsgaard, J., Jensen, H. N., & Toftegaard, T. S. (2017). A scalable cloud computing infrastructure for geospatial data analytics for change detection. 2017 Euromicro Conference on Digital System Design (DSD), (pp. 403–410). IEEE. https://doi.org/10.1109/DSD.2017.49
[15] Jamzuri, E. R., Mandala, H., Analia, R., & Susanto, S. (2022). Cloud-Based Architecture for YOLOv3 Object Detector using gRPC and Protobuf. Jurnal Teknik Elektro, 14(1), 18–23. https://doi.org/10.15294/jte.v14i1.36537
[16] Minailenko R., Sobinov O., Konoplitska-Slobodenyuk K., Buravchenko K. (2021). Architectural Features of Distributed Computing Systems. Central Ukrainian Scientific Bulletin. Technical Sciences, 35(4), 16–23. https://doi.org/10.32515/2664-262X.2021
[17] Sharvari, T., & Sowmya Nag, K. (2019). A study on Modern Messaging Systems – Kafka. RabbitMQ and NATS Streaming. https://doi.org/10.48550/arXiv.1912.03715
[18] Wu, H., Shang, Z., & Wolter, K. (2019, August). Performance prediction for the Apache Kafka messaging system. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Confeence on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), (pp. 154–161). IEEE. https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00036
[19] Levus, Y., Westermann, R., Morozov, M., Moravskyi, R., & Pustelnyk, P. (2022). Using Software Agents in a Distributed Computing System for Procedural Planetoid Terrain Generation. 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT). (pp. 446–449). IEEE. https://doi.org/10.1109/CSIT56902.2022.10000868
[20] Doran, J., & Parberry, I. (2010). Controlled procedural terrain generation using software agents. IEEE Transactions on Computational Intelligence and AI in Games, 2(2), 111–119. https://doi.org/10.1109/TCIAIG.2010.2049020
[21] Lim, F. Y., Tan, Y. W., & Bhojan, A. (2022). Visually improved erosion algorithm for the procedural generation of tile-based terrain. arXiv preprint arXiv:2210.14496. https://doi.org/10.5220/0010799700003124
[22] Gasch, C., Chover, M., Remolar, I., & Rebollo, C. (2020). Procedural modelling of terrains with constraints. Multimedia Tools and Applications, 79, 31125–31146. https://doi.org/10.1007/s11042-020-09476-3
[23] Mete, M. O., & Yomralioglu, T. (2021). Implementation of serverless cloud GIS platform for land valuation. International Journal of Digital Earth, 14(7), 836–850. https://doi.org/10.1080/17538947.2021.1889056
[24] Fanini, B., Pescarin, S., & Palombini, A. (2019). A cloud-based architecture for processing and dissemination of 3D landscapes online. Digital Applications in Archaeology and Cultural Heritage, 14, e00100. https://doi.org/10.1016/j.daach.2019.e00100