A Heuristic Algorithm for Multi-layer Network Optimization in Cloud Computing

One of the most important concepts in cloud computing is to model the problem as a multi-layer optimization problem, which leads to cost-savings in designing and operating the networks. The previous researchers have modeled the two-layer network-operating problem as an Integer Linear Programming (ILP) problem, and due to the computational complexity of solving it jointly, they have suggested a two-stage procedure in order to solve it by considering one layer at each stage. In this paper, considering the ILP model and using some of its properties, we propose a heuristic algorithm in order to solve the model jointly, considering the unicast, multicast, and anycast flows simultaneously. We first sort the demands in a decreasing order and use a greedy method in order to realize the demands in order. Due to the high computational complexity of the ILP model, the proposed heuristic algorithm is suitable for the networks with a large number of nodes. In this regard, various examples are solved by the CPLEX and MATLAB software. Our simulation results show that for the small values of and , CPLEX fails to find the optimal solution, while AGA finds a near optimal solution quickly. The proposed greedy algorithm could solve the large-scale networks approximately in polynomial time, and its approximation is reasonable.