Balancing distributed analytics' energy consumption using physics-inspired models

Brent Kraczek; Theodoros Salonidis; Prithwish Basu; Sayed Saghaian; Ali Sydney; Bongjun Ko; Tom LaPorta; Kevin Chan; James Lambert

doi:10.1117/12.2304485

9 May 2018 Balancing distributed analytics' energy consumption using physics-inspired models

Brent Kraczek, Theodoros Salonidis, Prithwish Basu, Sayed Saghaian, Ali Sydney, Bongjun Ko, Tom LaPorta, Kevin Chan, James Lambert

Author Affiliations +

Proceedings Volume 10652, Disruptive Technologies in Information Sciences; 1065206 (2018) https://doi.org/10.1117/12.2304485
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

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Citation Download Citation

Brent Kraczek, Theodoros Salonidis, Prithwish Basu, Sayed Saghaian, Ali Sydney, Bongjun Ko, Tom LaPorta, Kevin Chan, and James Lambert "Balancing distributed analytics' energy consumption using physics-inspired models", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065206 (9 May 2018); https://doi.org/10.1117/12.2304485

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