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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationDisruptive Technologies in Information Sciences
EditorsMisty Blowers, Russell D. Hall, Venkateswara R. Dasari
ISBN (Electronic)9781510618152
StatePublished - 2018
EventDisruptive Technologies in Information Sciences 2018 - Orlando, United States
Duration: Apr 17 2018Apr 18 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherDisruptive Technologies in Information Sciences 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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