The 2nd IEEE/ACM International Workshop on Network-Aware Big Data Computing (NEAC'20)

co-located with CCGrid'20, 11th - 14th Of May, Melbourne, Australia.

Download CFP


  • 2019.12.03 - A special issue in a journal is in the process of discussion (extended version from NEAC'20 will be highly recommended).

  • 2019.11.28 - The same as NEAC'19, there will be a Best Paper Award at NEAC'20.

  • 2019.11.15 - All accepted papers will be published in the Proceedings of the 20th IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing, published by IEEE.

  • 2019.11.08 - NEAC 2020 website is online.

About NEAC

Network communications is one of the main performance challenges for big data computing in large distributed systems such as datacenters, in terms of both communication time and energy consumption. Significant improvements have been achieved by using the state-of-the-art methods, designed in the research domains of data management (e.g., locality scheduling), data communications (e.g., flow scheduling) and network management (e.g., routing). However, almost all the techniques in their own fields just view each fields as a black box, and the additional performance gains from a co-optimization perspective have not yet been well explored. Moreover, in emerging data networks (e.g., DCNs with programmable switches or IoT networks), part of computation from end hosts can be offloaded into networks. This new paradigm can process data as it flows and have redefined the computation and communication in data processing, and thus how to optimize big data computing within the scheme becomes an interesting question.

NEAC aims to explore network-aware optimization opportunities for big data computing in distributed systems. It will bring researchers from related fields together to investigate innovative models, algorithms, architectures and systems to minimize data movement time, message traffic and energy consumption for big data computing in various network infrastructures, and consequently deliver significant performance improvements to the large-scale data analytics community

This workshop seeks interesting and innovative contributions and surveys on methods and designs covering all aspects of optimization for data computing, communication, message traffic and energy consumption in different network configurations. This workshop also encourages new initiatives of building bridges between big data computing and network communications. Topics of interest include, but are not limited to:

  • All network-aware optimization techniques for big data computing in distributed environments such as data locality, task, job, flow and routing scheduling in cluster, grid, edge and cloud.

  • All data-aware network designs such as protocols, domain-specific solutions and architectures for wireless networks, software-defined networks, data center networks, peer-to-peer networks, sensor networks, and Internet of Things.

  • All application and network co-design techniques for big data computing such as performance models, algorithms, programming paradigms, architectures and systems.

Detailed program of NEAC 2020 will be available in the middle of April 2020.


All contributions should be high quality, original and not published elsewhere or submitted for publication during the review period. Regular technical papers must be prepared in IEEE conference format and must not exceed 8 pages for full papers and 4 pages for short papers, and all submissions must be in English. Submissions that do not adhere to these guidelines or that violate formatting will be declined without review.

Submitted papers will be thoroughly reviewed by members of the Workshop Program Committee for quality, correctness, originality and relevance. All accepted papers must be presented by one of the authors, who must register. Papers must be submitted via the EasyChair online submission system: For further information regarding the NEAC 2020 submission, please contact the workshop co-organizer Long Cheng at

  • Submission Dealine: Feb 10th, 2020 Feb 14th, 2020

  • Author Notification: Feb 28th, 2020

  • Camera-Ready Due: Mar 20th, 2020

  • Workshop Date: May 11th, 2020



Long Cheng, Dublin City University, Ireland

John Murphy, University College Dublin, Ireland

Zhiming Zhao, University of Amsterdam, Netherlands

Program Committee

Aymen Azouz, Oracle, Ireland

Leandro Almeida, Federal Technological University of Parana, Brazil

Dick Epema, Delft University of Technology, Netherlands

Yang Hu, National University of Defense Technology, China

Zhuozhao Li, University of Chicago, USA

Cong Liu, Shandong University of Technology, China

Jinwei Liu, Florida A&M University, USA

Liam Murphy, University College Dublin, Ireland

Radu Prodan, University of Klagenfurt, Austria

Lukas Rupprecht, IBM Research Almaden, USA

Ilias Tachmazidis, University of Huddersfield, UK

Alexandru Uta, Vrije Universiteit Amsterdam, Netherlands

Shen Wang, University College Dublin, Ireland

Ying Wang, Institute of Computing Technology, CAS, China

Lei Yang, South China University of Technology, China

Murat Yilmaz, Dublin City University, Ireland

Dian Zhang, The Insight Centre for Data Analytics, Ireland

Publicity Chair

Madhusanka Liyanage, University College Dublin, Ireland

Ying Mao, Fordham University, USA

Publication Chair

Qingzhi Liu, Wageningen University & Research, Netherlands

Web Chair

Jose Juan Dominguez Veiga, Dublin City University, Ireland

Qishan Yang, Dublin City University, Ireland