TY - GEN
T1 - Balancing distributed analytics' energy consumption using physics-inspired models
AU - Kraczek, Brent
AU - Salonidis, Theodoros
AU - Basu, Prithwish
AU - Saghaian, Sayed
AU - Sydney, Ali
AU - Ko, Bongjun
AU - Laporta, Tom
AU - Chan, Kevin
AU - Lambert, James
N1 - Funding Information:
This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the ofcial policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049225449&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049225449&partnerID=8YFLogxK
U2 - 10.1117/12.2304485
DO - 10.1117/12.2304485
M3 - Conference contribution
AN - SCOPUS:85049225449
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Disruptive Technologies in Information Sciences
A2 - Blowers, Misty
A2 - Hall, Russell D.
A2 - Dasari, Venkateswara R.
PB - SPIE
T2 - Disruptive Technologies in Information Sciences 2018
Y2 - 17 April 2018 through 18 April 2018
ER -
