Since a decade ago, there has been a steady increase now, in the deployment and the universal utilization of web-based social media such as Video-on-demands, online games, and social networks in a way that the mass data produced by them now have a significant share of the whole Internet traffic. Users are more interested in content than its location. This issue led to a new concept for data management in networking called ICN, which in contrast to host-to-host paradigms of network communication, like IP networks, communications are based on the publishing and the subscribing of the most popular network contents. But, ICN could not be implemented due to the profound incompatibility it has with the current IP network. On the other hand, Software Define Network (SDN) has been introduced with an approach of separating the control and the data plane which has led to significant improvements in the flexibility and the management of networks and has made the deployment of ICNs feasible. The initial idea is to combine the two ICN and SDN technologies to create a content-oriented internet to have effective control and management over the networks. However, this new approach, itself gives rise to a new challenge that is the identification of the most appropriate places for data storage in the topology of the network. In this research, we investigate the management of contents and traffic in ICN using an SDN's Central Controller with the goal of serving the requests by the nearest servers and simultaneously distributing and balancing traffic among all links and routers so the congestions are prevented. For this purpose, we first formulate our model as a mixed-integer non-linear programming problem. Then we try to find the solution in an Offline mode, in which all the requests are known. In advance, we use GAMS commercial software with a random topology and we compare the outcomes with the ones of IP networks. In this scenario, the results are developed in a way that the contents are saved on the nearest server or the available servers and the most optimized routes from the edge node to the servers are constructed. In this case, the results show that the proposed model consumes up to 20\\% less power than traditional networks and increases network efficiency by up to 50\\%. Next, we consider a dynamic scenario, however, since MINLP problems are categorized as NP-Hard problems and finding an optimal solution in GAMS are either time-consuming or infeasible, we have proposed a time-scaled approximate algorithm which reduces the computational complexities. At last, to demonstrate the capabilities and performance of this algorithm, we compare our results to the results of the GAMS and we show that our algorithm has extensively less computational time, while the outcomes accurately resemble the outcomes of the GAMS as a semi optimal solution. Key Words : Software-Defined Network, Information-Centric Network, Caching, Load balancing, Power saving