Recursive quadratic programming for constrained nonlinear optimization of session throughput in multiple‐flow network topologies

Abstract

Abstract In this article, we propose and formulate a single‐objective nonlinearly constrained programming problem with delay and capacity constraints. Specifically, we consider the utility resource allocation with the goal of maximizing the throughput amongst the multiple heterogeneous sessions in the network. Several numerical experiments for multiple topologies exhibit the performance and global convergence process of the proposed iterative algorithm. These network topologies are considered with distinct source‐destination communication sessions to emphasize the traffic flow analysis and optimal rate allocation vectors. For this, we employ recursive quadratic programming method and Lagrangian multiplier approach to solve the proposed optimization problem. The presented numerical results are obtained using Octave simulation tool. The results obtained from implementation of numerical simulation demonstrate the robustness and convergence performance of the proposed optimization scheme to optimal solution within finite number of iterations. Finally, we employ various global measures of fairness including the entropy‐based index, G's fairness index, linear product‐based fairness index, and Bossaer's fairness index to assess the performance of the proposed resource allocation scheme.

Keywords

• fairness measure
• nonlinear constrained optimization
• optimal value function
• recursive quadratic programming