Pervasive smart meters that continuously measure power usage by consumers
within a smart (power) grid are providing utilities and power systems
researchers with unprecedented volumes of information through streams
that need to be processed and analyzed in near realtime. We introduce
the use of Cloud platforms to perform scalable, latency sensitive
stream processing for eEngineering applications in the smart grid
domain. One unique aspect of our work is the use of adaptive rate
control to throttle the rate of generation of power events by smart
meters, which meets accuracy requirements of smart grid applications
while consuming 50% lesser bandwidth resources in the Cloud.
%0 Conference Paper
%1 Simmhan:sciencecloud:2011
%A Simmhan, Yogesh
%A Cao, Baohua
%A Giakkoupis, Michail
%A Prasanna, Viktor K.
%B International Workshop on Scientific Cloud Computing (ScienceCloud)
%D 2011
%I ACM
%K cloud, grid, paper peer reviewed, short smart streaming, usc,
%P 33--38
%R 10.1145/1996109.1996116
%T Adaptive rate stream processing for smart grid applications on clouds
%U http://ceng.usc.edu/~simmhan/pubs/simmhan-sciencecloud-2011.pdf
%X Pervasive smart meters that continuously measure power usage by consumers
within a smart (power) grid are providing utilities and power systems
researchers with unprecedented volumes of information through streams
that need to be processed and analyzed in near realtime. We introduce
the use of Cloud platforms to perform scalable, latency sensitive
stream processing for eEngineering applications in the smart grid
domain. One unique aspect of our work is the use of adaptive rate
control to throttle the rate of generation of power events by smart
meters, which meets accuracy requirements of smart grid applications
while consuming 50% lesser bandwidth resources in the Cloud.
%@ 978-1-4503-0699-7
@inproceedings{Simmhan:sciencecloud:2011,
abstract = {Pervasive smart meters that continuously measure power usage by consumers
within a smart (power) grid are providing utilities and power systems
researchers with unprecedented volumes of information through streams
that need to be processed and analyzed in near realtime. We introduce
the use of Cloud platforms to perform scalable, latency sensitive
stream processing for eEngineering applications in the smart grid
domain. One unique aspect of our work is the use of adaptive rate
control to throttle the rate of generation of power events by smart
meters, which meets accuracy requirements of smart grid applications
while consuming 50% lesser bandwidth resources in the Cloud.},
added-at = {2014-08-13T04:08:36.000+0200},
author = {Simmhan, Yogesh and Cao, Baohua and Giakkoupis, Michail and Prasanna, Viktor K.},
biburl = {https://www.bibsonomy.org/bibtex/21266bc6575ed675d1ba35eb09962ed86/simmhan},
booktitle = {International Workshop on Scientific Cloud Computing (ScienceCloud)},
doi = {10.1145/1996109.1996116},
interhash = {eebe8705f778786b411342471b0f10a6},
intrahash = {1266bc6575ed675d1ba35eb09962ed86},
isbn = {978-1-4503-0699-7},
keywords = {cloud, grid, paper peer reviewed, short smart streaming, usc,},
location = {San Jose, California, USA},
month = {June},
owner = {Simmhan},
pages = {33--38},
publisher = {ACM},
timestamp = {2014-08-13T04:08:36.000+0200},
title = {Adaptive rate stream processing for smart grid applications on clouds},
url = {http://ceng.usc.edu/~simmhan/pubs/simmhan-sciencecloud-2011.pdf},
year = 2011
}