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Balancing distributed analytics' energy consumption using physics-inspired models
Author(s): Brent Kraczek; Theodoros Salonidis; Prithwish Basu; Sayed Saghaian; Ali Sydney; Bongjun Ko; Tom LaPorta; Kevin Chan; James Lambert
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Paper Abstract

With the rise of small, networked sensors, the volume of data generated increasingly require curation by AI to analyze which events are of sufficient importance to report to human operators. We consider the ultimate limit of edge computing, when it is impractical to employ external resources for the curation, but individual devices have insufficient computing resources to perform the analytics themselves. In a previous paper we introduced a decenralized method that distributes the analytics over the network of devices, employing simulated annealing, based on physics-inspired Metropolis Monte Carlo. If the present paper we discuss the capability of this method to balance the energy consumption of the placement on a network of heterogeneous resources. We introduce the balanced utilization index (BUI), an adaptation of Jain’s Fairness Index, to measure this balance.

Paper Details

Date Published: 9 May 2018
PDF: 11 pages
Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065206 (9 May 2018); doi: 10.1117/12.2304485
Show Author Affiliations
Brent Kraczek, U.S. Army Research Lab. (United States)
Theodoros Salonidis, Raytheon BBN Technologies (United States)
Prithwish Basu, IBM Thomas J. Watson Research Ctr. (United States)
Sayed Saghaian, The Pennsylvania State Univ. (United States)
Ali Sydney, Raytheon BBN Technologies (United States)
Bongjun Ko, IBM Thomas J. Watson Research Ctr. (United States)
Tom LaPorta, The Pennsylvania State Univ. (United States)
Kevin Chan, U.S. Army Research Lab. (United States)
James Lambert, Defence Science and Technology Lab. (United Kingdom)


Published in SPIE Proceedings Vol. 10652:
Disruptive Technologies in Information Sciences
Misty Blowers; Russell D. Hall; Venkateswara R. Dasari, Editor(s)

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