Fast In-Network Functionality Embedding in Software-Defined Service-Centric Networking

Joint resource allocation in integrated networking, computing, and caching frameworks has attracted plenty of attention. In software-defined service-centric networking (SDSCN), we investigate an energy cost economical functionality embedding (FE) problem. The FE problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is difficult to obtain its global optimal solutions. In this paper, we propose a low-complexity high-performance algorithm for energy-economical FE design in large-scale SDSCN systems by leveraging the alternating direction method of multipliers (ADMM) together with successive convex approximation (SCA). In specific, the FE problem is first approximated as a sequence of convex subproblems via SCA. Each convex subproblem is then reformulated as a novel ADMM form to enable parallel computations and closed-form solutions. Numerical results show that our fast algorithm reduces the complexity by orders of magnitude and obtains favorable performances compared with state-of-the-art algorithms.