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

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