2019 International Workshop on Network-Aware Big Data Computing (NEAC)

co-located with CCGrid'19, 14th - 17th Of May, Larnaca, Cyprus

Download CFP


  • 2018.12.13 - There will be a gift for the speaker of the Best Paper at NEAC 2019.

  • 2018.11.28 - There will be a Best Paper Award at NEAC 2019 and selected papers will be invited for a fast track review by Cluster Computing journal (IF: 1.601), published by Springer.

  • 2018.11.28 - All accepted papers will be published in the Proceedings of the 19th IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing, published by IEEE.

  • 2018.11.28 - NEAC 2019 website is online.

About NEAC

Efficient big data computing is still challenging current techniques. One of the main performance challenges is the network communication. The reason is that the performance of CPU has grown much faster than network bandwidth in recent years and, as such, the network creates a bottleneck to computation. Significant performance improvements on big data computing have been achieved by using state-of-the-art methods, such as locality and task scheduling in the distributed data management domain, and data flow scheduling in the data communications domain. However, almost all the techniques in these two fields just view each other as a black box, and the additional performance gains from a co-optimization perspective have not yet been explored.

NEAC aims to bridge the gap of current research in big data computing and network communications. It will bring researchers from related fields together to explore innovative models, algorithms, architectures and systems to minimize data movement time, message traffic and energy consumption for big data computing, 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 2019 will be available in the middle of April 2019.


All contributions should be 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: https://www.easychair.org/conferences/?conf=neac2019. For further information regarding the NEAC 2019 submission, please contact the workshop co-organizer Long Cheng at long.cheng@ucd.ie.

  • Submission Dealine: Feb 28th, 2019

  • Author Notification: Mar 20th, 2019

  • Camera-Ready Due: Mar 28th 2019

  • Workshop Date: May 14th 2019



Long Cheng, University College Dublin, Ireland

John Murphy, University College Dublin, Ireland

Program Committee

Leandro Almeida, Federal Technological University of Parana, Brazil

Dick Epema, Delft University of Technology, Netherlands

Zhuozhao Li, University of Chicago, USA

Qingzhi Liu, Eindhoven University of Technology, Netherlands

Liam Murphy, University College Dublin, Ireland

Bogdan Nicolae, Argonne National Laboratory, USA

Lukas Rupprecht, IBM Research Almaden, USA

Georgios Theodoropoulos, Southern University of Science and Technology, China

Lei Yang, South China University of Technology, China

Zhiming Zhao, University of Amsterdam, Netherlands