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.