NEAC@CCGrid 2019 : International Workshop on Network-Aware Big Data Computing@CCGrid
Call For Papers
CFP: 2019 International Workshop on Network-Aware Big Data Computing (NEAC)
co-located with CCGrid'19, 14th - 17th Of May, Larnaca, Cyprus
There will be a gift for the speaker of the Best Paper at NEAC 2019, and selected papers will be invited for a fast track review by Cluster Computing journal.
Regular technical papers must be prepared in IEEE conference format. Full papers are limited to 8 pages. Short papers with novel ideas with 2-4 pages are also encouraged.
Submission deadline: March 8th, 2019
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.
NEAC 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. It 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.
-- 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
-- Long Cheng, University College Dublin, Ireland
-- John Murphy, University College Dublin, Ireland