NEO-QE TERMINATED
NEtwork-aware Optimization for Query Executions in Large Systems
Total Cost €
0EC-Contrib. €
0Partnership
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0NEO-QE project word cloud
Explore the words cloud of the NEO-QE project. It provides you a very rough idea of what is the project "NEO-QE" about.
Project "NEO-QE" data sheet
The following table provides information about the project.
| Coordinator |
UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Organization address contact info |
| Coordinator Country | Ireland [IE] |
| Total cost | 187˙866 € |
| EC max contribution | 187˙866 € (100%) |
| Programme |
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility) |
| Code Call | H2020-MSCA-IF-2017 |
| Funding Scheme | MSCA-IF-EF-ST |
| Starting year | 2018 |
| Duration (year-month-day) | from 2018-07-01 to 2020-06-30 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN | IE (DUBLIN) | coordinator | 187˙866.00 |
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Project objective
In data-intensive environments such as data warehouses, efficient execution of query operations is crucial for the overall performance of a system. One of the main performance challenges in such scenarios is the network communications. Significant performance improvements have been achieved by using state-of-the-art methods, designed in the data management and data communication domain. However, the proposed techniques in both fields just view each other as a black box, and the additional gains in performance from a co-optimization perspective have not yet been explored. In this project, I will focus on the design and development of a novel query execution system that can bridge the gap of co-optimization between high-level query executions and low-level data communications. Such a system will be highly efficient and robust in the presence of different workloads and network configurations in large systems, and consequently deliver significant performance improvements to the large scale data-analytics community. In the meantime, the success of the project will also aid my career development through an increased research profile and collaboration with industry, and enhance the knowledge and networks of UCD.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2020 |
Xuan Chen, Long Cheng, Cong Liu, Qingzhi Liu, Jinwei Liu, Ying Mao, John Murphy A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems published pages: 1-12, ISSN: 1932-8184, DOI: 10.1109/jsyst.2019.2960088 |
IEEE Systems Journal | 2020-02-05 |
| 2019 |
Long Cheng, Spyros Kotoulas, Qingzhi Liu, Ying Wang Load-balancing distributed outer joins through operator decomposition published pages: 21-35, ISSN: 0743-7315, DOI: 10.1016/j.jpdc.2019.05.008 |
Journal of Parallel and Distributed Computing 132 | 2019-10-29 |
| 2018 |
Long Cheng, John Murphy, Qingzhi Liu, Chunliang Hao, Georgios Theodoropoulos Minimizing Network Traffic for Distributed Joins Using Lightweight Locality-Aware Scheduling published pages: 293-305, ISSN: , DOI: 10.1007/978-3-319-96983-1_21 |
Euro-Par 2018: Parallel Processing | 2019-10-29 |
| 2019 |
Long Cheng, Boudewijn Van Dongen, Wil Van Der Aalst Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment published pages: 1-1, ISSN: 1939-1374, DOI: 10.1109/tsc.2019.2906203 |
IEEE Transactions on Services Computing | 2019-10-29 |
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