Optimal Resource Allocation in Next Generation Network Services using Engineering Optimization with Linear Constraint Particle Swarm

In this paper, we consider the problem of pricing for optimal resource allocation service using Engineering Optimization with Particle Swarm algorithm that ensures efficient resource allocation that provides guaranteed quality of service while maximizing profit in multiservice networks. We formulate our generalized optimization algorithm based on the notion of a "profit center" with an arbitrary number of service classes, linear revenue and nonlinear cost functions and general performance constraints. To ensure the resource constraint is satisfied, we incorporate adaptive resource bounds to guide the search. Specifically, we develop a fast, low complexity algorithm for online dynamic resource allocation, and examine its properties. Finally, its performance is evaluated through an extensive numerical study.