On distributed information composition in big data systems
Document Type
Conference Proceeding
Publication Date
9-1-2019
Abstract
Modern big data computing systems exemplified by Hadoop employ parallel processing based on distributed storage. The results produced by parallel tasks such as computing modules in scientific workflows or reducers in the MapReduce framework are typically stored in a distributed file system across multiple data nodes. However, most existing systems do not provide a mechanism to compose such distributed information, as required by many big data applications. We construct analytical cost models and formulate a Distributed Information Composition problem in Big Data Systems, referred to as DIC-BDS, to aggregate multiple datasets stored as data blocks in Hadoop Distributed File System (HDFS) using a composition operator of specific complexity to produce one final output. We rigorously prove that DIC-BDS is NP-complete, and propose two heuristic algorithms: Fixed-window Distributed Composition Scheme (FDCS) and Dynamic-window Distributed Composition Scheme with Delay (DDCS-D). We conduct extensive experiments in Google clouds with various composition operators of commonly considered degrees of complexity including O(n), O(n log n), and O(n2). Experimental results illustrate the performance superiority of the proposed solutions over existing methods. Specifically, FDCS outperforms all other algorithms in comparison with a composition operator of complexity O(n) or O(n log n), while DDCS-D achieves the minimum total composition time with a composition operator of complexity O(n2). These algorithms provide an additional level of data processing for efficient information aggregation in existing workflow and big data systems.
Identifier
85083267334 (Scopus)
ISBN
[9781728124513]
Publication Title
Proceedings IEEE 15th International Conference on Escience Escience 2019
External Full Text Location
https://doi.org/10.1109/eScience.2019.00025
First Page
168
Last Page
177
Grant
2019C03138
Fund Ref
National Science Foundation
Recommended Citation
Alquwaiee, Haifa; He, Songlin; Wu, Chase; Tang, Qiang; and Shen, Xuewen, "On distributed information composition in big data systems" (2019). Faculty Publications. 7360.
https://digitalcommons.njit.edu/fac_pubs/7360
