Optimization of Virtualization Cost, Processing Power and Network Load of 5G Software-Defined Data Centers

Abstract

Virtualization is getting unprecedented attention from Mobile Network Operators (MNOs) as it provides agility in deployment, especially when coupled with the Cloud that offers inherent elasticity and load-balancing of resources. MNOs have to ensure operational excellence by meeting several objectives. In this context, we propose in this paper, a framework for optimizing the mapping of next Generation Node-Bs (gNBs) to Software-Defined 5G Core (5GC) delay tolerant Network Functions (NFs). These NFs are considered to be deployed as a Virtual Machine (VM) pool, or containers, in order to minimize cloud computing cost, processing power and at the same time maximize network load. First, we formulate this problem as an integer linear program, while taking into account multiple constraints including Virtual Central Processing Unit (vCPU) capacity, central processing load limits and integrality of mapping relations between gNBs and 5GC NFs. Then, we propose an algorithm to solve large problem instances based on Branch, Cut and Price (BCP) combining all of "Branch and Price", "Branch and Cut" and "Branch and Bound" frameworks. We present several schemes reflecting different optimization goals that the MNO can foster: virtualization cost, power minimization, network load or all. Simulation results demonstrate the good performance of our proposed algorithm to solve the gNBs-VM pool mapping for all evaluated schemes, while also emphasizing the advantages of a particular one (EWoS-333 for Equal Weight optimization Scheme) that can decrease virtualization cost by almost one order of magnitude compared to a static selection scheme, while considering the other two objectives.

Column Generation for Optimization Problems in Communication Networks

“…• A heuristic algorithm for the last RMP. Data center network [12] Dynamic mapping of gNB to the software virtual machine pool. the search tree cutting, and a heuristic algorithm is designed for the last RMP to obtain tighter UB.…”

“…5a and 5b, it is observed that the result of BP can obtain the optimal solution with low time cost, while CG can reach a near-optimal result with lower computational complexity. 8) BP for Software Defined Data Center Network: A dynamic mapping of next generation node-Bs (gNBs) to software virtual machine pool problem is proposed in [12], with the weighted objective to minimize the total cloud computing cost, processing power and to maximize the network traffic load processed by virtual machine pools. BP is employed for handling the problem, with the PP to generate the mapping association and RMP to decide the optimal mapping set.…”