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  4. Classifying Serialization Formats for Inter-service Communication in Distributed Systems

Classifying Serialization Formats for Inter-service Communication in Distributed Systems

ACPS.
2024;
Volume 9, Number 2
: pp. 175 - 180
Authors:
  1. Eduard Maltsev
  2. Oleksandr Muliarevych
  3. Asmad Razzaque
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University
3
Sapienza University of Rome

This study focuses on classifying serialization formats used in inter-service communication (ISC) within distributed systems and exploring their historical development. We have examined key features of humanreadable formats such as XML, JSON, and YAML, binary formats like Protocol Buffers and Apache Avro, and columnar formats such as Apache Parquet and ORC, among others. Our results have indicated a significant shift toward binary formats optimized for speed and compactness in recent years. The industry demand score for Apache Avro and Google Protocol Buffers has been shown to be much higher than for Thrift. JSON remains on top, showing the best score for general technology adoption and industry demand score; Zero-copy formats like Can’n proto and Flatbuffers show lower industry demand scores in comparison to AVRO and Protocol Buffers but are
useful in specific scenarios.1

Big Data applications
Data commu- nication
distributed processing
encoding
information exchange
Protocols
software architecture
  1.  Adibfar, A., Costin, A., (2021). Review of Data Serialization Challenges and Validation Methods for Improving Interoperability in Computing. In Civil Engineering, 522–529. DOI: 10.1061/9780784483893.065.
  2. Friesel, D. and Spinczyk, O., (2021). Data Serialization Formats for the Internet of Things. Electronic Communications     of     the      EASST,      vol.      80. DOI: 10.14279/TUJ.ECEASST.80.1134.1078.
  3. Luis, Á., Casares, P., Cuadrado-Gallego, J., Patricio, M., (2021). PSON: A Serialization Format for IoT Sensor Networks.    Sensors,    vol.    21,    no.     13,     4559. DOI: 10.3390/s21134559.
  4. Maltsev, E., Muliarevych, O., (2024). Beyond JSON: Evaluating Serialization Formats for Space-Efficient Communication. Advances in Cyber-Physical Systems, vol. 9, no. 1, 9–15. DOI: 10.23939/acps2024.01.009.
  5. Morschel, L., (2020). dCache – Efficient Message Encoding For Inter-Service Communication in dCache: Evaluation of Existing Serialization Protocols as a Replacement for Java Object Serialization. EPJ Web Conf., vol. 245,  05017. DOI: 10.1051/epjconf/202024505017.
  6. Kumar, P., Agarwal, R., Shivaprasad, R., Sitaram, D., Kalambur, S., (2021). Performance Characterization of Communication Protocols in Microservice Applications. In 2021 International Conference on Smart Applications, Communications and Networking (SmartNets), Glasgow,1– 5. DOI: 10.1109/SmartNets50376.2021.9555425.
  7. Mooney, P., Minghini, M., (2022). GEOSPATIAL DATA EXCHANGE USING BINARY DATA SERIALIZATION APPROACHES.   Int.   Arch.   Photogram.   Remote   Sens. Spatial   Inf.   Sci.,   vol.   XLVIII-4/W1-2022,   307–313. DOI: 10.5194/isprs-archives-XLVIII-4-W1-2022-307- 2022.
  8. Tiwary, G., Stroulia, E., Srivastava,  A.,  (2021). Compression of XML and JSON API Responses. IEEE Access,         vol.          9,                             57426–57439. DOI: 10.1109/ACCESS.2021.3073041.
  9. Weerasinghe, S., Perera, I., (2022). Evaluating the Inter- Service Communication on Microservice  Architecture. In 2022 7th International Conference on Information Technology               Research     (ICITR),  1–6.DOI: 10.1109/ICITR57877.2022.9992918.
  10. Parashar, A., Anand, P., Abraham, A., (2020). Performance Analysis and Optimization of Serialization Techniques for Deep Neural Networks. In  Computer Vision, Pattern Recognition, Image Processing, and Graphics, 250–260. DOI: 10.1007/978-981-15-8697-2_23.