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Distributed Programming over Time-series Graphs

Simmhan, Yogesh and Choudhury, Neel and Wickramaarachchi, Charith and Kumbhare, Alok and Frincu, Marc and Raghavendra, Cauligi and Prasanna, Viktor (2015) Distributed Programming over Time-series Graphs. In: 29th IEEE International Parallel and Distributed Processing Symposium (IPDPS), MAY 25-29, 2015, Hyderabad, INDIA, pp. 809-818.

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Official URL: http://dx.doi.org/10.1109/IPDPS.2015.66


Graphs are a key form of Big Data, and performing scalable analytics over them is invaluable to many domains. There is an emerging class of inter-connected data which accumulates or varies over time, and on which novel algorithms both over the network structure and across the time-variant attribute values is necessary. We formalize the notion of time-series graphs and propose a Temporally Iterative BSP programming abstraction to develop algorithms on such datasets using several design patterns. Our abstractions leverage a sub-graph centric programming model and extend it to the temporal dimension. We present three time-series graph algorithms based on these design patterns and abstractions, and analyze their performance using the GoFFish distributed platform on Amazon AWS Cloud. Our results demonstrate the efficacy of the abstractions to develop practical time-series graph algorithms, and scale them on commodity hardware.

Item Type: Conference Proceedings
Series.: International Parallel and Distributed Processing Symposium IPDPS
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 22 Oct 2016 08:59
Last Modified: 22 Oct 2016 08:59
URI: http://eprints.iisc.ac.in/id/eprint/54962

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