An Efficient Decomposition Algorithm for Large-Scale Network Slicing

In this paper, we consider the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. We propose an efficient decomposition algorithm for solving this NP-hard problem. The proposed algorithm decomposes the large-scale hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined; and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the global solution of the NS problem. We demonstrate the effectiveness and efficiency of the proposed algorithm via numerical experiments.Comment: 5 pages, 4 figures, accepted for publication in IEEE SPAWC 202

Tillage condition effects on soil/plow-breast flow interaction of a horizontally reversible plow

Abstract : The horizontally reversible plow (HRP) is commonly utilized because of higher performances than the regular mold-board plow. Soil/plow surface flow interaction during HRP tillage trends to incur so severe pressure on the plow-breast as to reduce the plow life. This paper numerically characterized the soil/plow-breast flow interaction and subsequently assessed tillage-condition effects on the plow-breast surface. These tillage conditions herein involved tool speed and operation-al depth. The simulations showed that for either tool speed or operational depth the maximum pressure appeared at the plow-shank of the plow-breast and that the soil pressures were increased with them. The computational fluid dynamics (CFD) based predictions qualitatively agreed with the preliminary experimental results at the identified settings with scanning electronic microscopy. Once again, CFD analysis is demonstrated to be feasible and effective enough to provide insight into improve the horizontally reversible plow by predicting real soil behaviors