Intelligent Optimization and Fair Resource Allocation in Constrained Digital and Cyber-Physical Systems

Abstract

Resource allocation in constrained digital and cyber-physical systems (CPS) increasingly must satisfy two competing requirements: near-real-time performance under tight compute, network, and energy budgets, and transparent fairness guarantees across heterogeneous users, applications, and control loops. This paper develops a system-oriented optimization framework for fair and intelligent resource allocation that unifies (i) operational constraints typical of em- bedded and edge platforms (limited CPU cycles, shared wireless bandwidth, and energy caps), (ii) stability- and safety-relevant constraints arising from closed-loop CPS dynamics, and (iii) fairness criteria that are meaningful for both digital services (throughput/latency parity) and physical processes (risk- and constraint-violation parity). We cast the problem as a constrained stochastic program with time-coupled dynamics and propose a modular approach that combines a predictive layer for short-horizon demand/dynamics estimation with a primal–dual allocation layer enforcing feasibility and fairness via Lagrange multipliers. The method supports multiple fairness notions—max–min, proportional, and risk-sensitive fairness—and exposes their trade- offs with latency, energy, and control performance. Using a suite of representative case studies (edge inference serving, wireless scheduling for mixed-criticality traffic, and networked control with shared computation), we demonstrate that fairness constraints can be enforced with modest efficiency loss when the allocation mechanism is explicitly co-designed with system constraints. We also identify failure modes in which naive fairness regularization destabilizes control or am- plifies queueing delay, motivating a set of practical design rules for deploying fairness-aware optimization in constrained CPS.