Zero-Change Object Transmission for Distributed Big Data Analytics


Mingyu Wu, Shuaiwei Wang, Haibo Chen, and Binyu Zang, Shanghai Jiao Tong University


Distributed big-data analytics heavily rely on high-level languages like Java and Scala for their reliability and versatility. However, those high-level languages also create obstacles for data exchange. To transfer data across managed runtimes like Java Virtual Machines (JVMs), objects should be transformed into byte arrays by the sender (serialization) and transformed back into objects by the receiver (deserialization). The object serialization and deserialization (OSD) phase introduces considerable performance overhead. Prior efforts mainly focus on optimizing some phases in OSD, so object transformation is still inevitable. Furthermore, they require extra programming efforts to integrate with existing applications, and their transformation also leads to duplicated object transmission. This work proposes Zero-Change Object Transmission (ZCOT), where objects are directly copied among JVMs without any transformations. ZCOT can be used in existing applications with minimal efforts, and its object-based transmission can be used for deduplication. The evaluation on state-of-the-art data analytics frameworks indicates that ZCOT can greatly boost the performance of data exchange and thus improve the application performance by up to 23.6%.

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@inproceedings {280780,
author = {Mingyu Wu and Shuaiwei Wang and Haibo Chen and Binyu Zang},
title = {{Zero-Change} Object Transmission for Distributed Big Data Analytics},
booktitle = {2022 USENIX Annual Technical Conference (USENIX ATC 22)},
year = {2022},
isbn = {978-1-939133-29-59},
address = {Carlsbad, CA},
pages = {137--150},
url = {},
publisher = {USENIX Association},
month = jul

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