Liang Zhang
Journal Articles
Syed Asif Raza, Wenji Wu, Qiming Lu, Liang Zhang, Sajith Sasidharan, Phil DeMar, Chin Guok, John Macauley, Eric Pouyoul, Jin Kim, Seo-Young Noh, “AmoebaNet: An SDN-enabled network service for big data science”, Journal of Network and Computer Applications, Elsevier, October 1, 2018, 119:70-82,
Liang Zhang, Wenji Wu, Phil DeMar, “mdtmFTP and its evaluation on ESNET SDN testbed”, Future Generation Computer Systems, Elsevier, February 1, 2018, 79:199-204,
Conference Papers
Wenji Wu, Liang Zhang, Qiming Lu, Phil DeMar, Robert Illingworth, Joe Mambretti, Se-Young Yu, Jim Hao Chen, Inder Monga, Xi Yang, Tom Lehman, Chin Guok, John MacAuley, “ROBIN (RuciO/BIgData Express/SENSE) A Next-Generation High-Performance Data Service Platform”, 2020 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS), IEEE/ACM, December 31, 2020,
Qiming Lu, Liang Zhang, Sajith Sasidharan, Wenji Wu, Phil DeMar, Chin Guok, John Macauley, Inder Monga, Se-Young Yu, Jim Hao Chen, Joe Mambretti, Jin Kim, Seo-Young Noh, Xi Yang, Tom Lehman, Gary Liu, “BigData Express: Toward Schedulable, Predictable, and High-Performance Data Transfer”, 2018 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS), IEEE/ACM, February 24, 2019,
Liang Zhang, Phil Demar, Bockjoo Kim, Wenji Wu, “MDTM: Optimizing Data Transfer Using Multicore-Aware I/O Scheduling”, 2017 IEEE 42nd Conference on Local Computer Networks (LCN), IEEE, September 12, 2017,
Bulk data transfer is facing significant challenges in the coming era of big data. There are multiple performance bottlenecks along the end-to-end path from the source to destination storage system. The limitations of current generation data transfer tools themselves can have a significant impact on end-to-end data transfer rates. In this paper, we identify the issues that lead to underperformance of these tools, and present a new data transfer tool with an innovative I/O scheduler called MDTM. The MDTM scheduler exploits underlying multicore layouts to optimize throughput by reducing delay and contention for I/O reading and writing operations. With our evaluations, we show how MDTM successfully avoids NUMA-based congestion and significantly improves end-to-end data transfer rates across high-speed wide area networks.
