Stochastic optimization of inference execution on tactical edge networks
Mikal Willeke, Sean Harding, Kevin Chan, Nathaniel D. Bastian · 2024
In tactical edge networks, the volatility of computational and communication resources complicates the consistent processing of data. Previous work has developed a system that allows execution of inference task applications throughout a network using a variety of adaptations to machine learning models that offer accuracy and latency trade-offs as conditions change in order adaptively perform deterministic resource allocation at the tactical edge. This paper expands on previous work that proposed a system that allows execution of inference task applications throughout a network and then developed a resource allocation algorithm that optimally places intelligence, surveillance, and reconnaissance related machine learning tasks throughout the network. We propose utilizing stochastic optimization to analyze the computational time and performance for inference tasks. Instead of adhering to deterministic averages, we use sample average approximation as a solution technique to optimize and analyze the inherent uncertainties of tactical edge environments, optimizing over the distribution of inference data rather than average-case scenarios. This paper verifies Jensen’s inequality gap within deterministic optimization and proposes an improved resource allocation algorithm that optimally places tasks throughout a network under uncertainty. We present initial results on a military relevant tactical edge network scenario.