Abstract:
With the expansion of mobile devices and new trends in mobile communication technologies, there is an increasing demand for diversified services. To accommodate a large number of services on a common network, it becomes crucial for an operator to optimize resource allocation decisions to satisfy the service requirements in an economical way. In this thesis, the computation architecture design problem is considered first where server placement, service deployment, and task assignment decisions are optimized to maximize the revenue of the operator. The problem is modeled as a mixed-integer linear programming (MILP) formulation and a Lagrangian relaxation- based heuristic algorithm is proposed. Then, the concept of network slicing, which partitions a single physical network into multiple isolated slices, is examined. In the deterministic network slicing problem, the capacities of the computational resources are partitioned into slices each of which is customized for a particular service type. An MILP formulation is presented that takes the delay requirements of services into account. Additionally, two algorithms based on Benders decomposition are devised along with some valid inequalities and cut generation techniques. The problem definition is also extended to consider the stochastic behavior of the service requests. A two-stage stochastic integer programming model is constructed which is then converted into a large-scale MILP model by defining a set of scenarios for the random parameters. A similar decomposition approach is also applied to the stochastic network slicing problem. In our computational study on randomly generated test instances, the validity of our models is assessed and the effectiveness of the proposed solution approaches is demonstrated.
