@article{Li2022, 
author = {Le-Le Li and Jiang-Yi Liu and Jian-Ping Fan and Xue-Hai Qian and Kai Hwang and Yeh-Ching Chung and Zhi-Bin Yu},
title = {SOCA-DOM: A Mobile System-on-Chip Array System for Analyzing Big Data on the Move},
year = {2022},
journal = {Journal of Computer Science and Technology},
volume = {37},
number = {6},
pages = {1271-1289},
keywords = {edge computing, mobile architecture, resource management, big data analytics, software-defined system},
url = {https://www.sciopen.com/article/10.1007/s11390-022-1087-z},
doi = {10.1007/s11390-022-1087-z},
abstract = {Recently, analyzing big data on the move is booming. It requires that the hardware resource should be low volume, low power, light in weight, high-performance, and highly scalable whereas the management software should be flexible and consume little hardware resource. To meet these requirements, we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier “software-defined” resource manager named Chameleon. First, we design an Ethernet communication board to support an array of mobile system-on-chips. Second, we propose a two-tier software architecture for Chameleon to make it flexible. Third, we devise data, configuration, and control planes for Chameleon to make it “software-defined” and in turn consume hardware resources on demand. Fourth, we design an accurate synthetic metric that represents the computational power of a computing node. We employ 12 Apache Spark benchmarks to evaluate SOCA-DOM. Surprisingly, SOCA-DOM consumes up to 9:4x less CPU resources and 13.5x less memory than Mesos which is an existing resource manager. In addition, we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon servers. Based on the results, we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.}
}