Controlling the Trade-Off between Resource Efficiency and User Satisfaction in NDNs Based on Naive Bayes Data Classification and Lagrange Method  
[PERSON]  
1 Laboratoire d'Informatique et de Mathematiques, Universite Amar Teldij de Laghouat, Laghouat 03000, Algeria; [EMAIL_ADDRESS] (A.T.H.); [EMAIL_ADDRESS] (C.A.K.); [EMAIL_ADDRESS] (B.Z.); [EMAIL_ADDRESS] (N.L.); [EMAIL_ADDRESS] (A.e.K.T.)  
[PERSON]  
1 Laboratoire d'Informatique et de Mathematiques, Universite Amar Teldij de Laghouat, Laghouat 03000, Algeria; [EMAIL_ADDRESS] (A.T.H.); [EMAIL_ADDRESS] (C.A.K.); [EMAIL_ADDRESS] (B.Z.); [EMAIL_ADDRESS] (N.L.); [EMAIL_ADDRESS] (A.e.K.T.)  
[PERSON]  
1 Laboratoire d'Informatique et de Mathematiques, Universite Amar Teldij de Laghouat, Laghouat 03000, Algeria; [EMAIL_ADDRESS] (A.T.H.); [EMAIL_ADDRESS] (C.A.K.); [EMAIL_ADDRESS] (B.Z.); [EMAIL_ADDRESS] (N.L.); [EMAIL_ADDRESS] (A.e.K.T.)  
[PERSON]  
2 Computer Engineering Department (DISCA), Universitat Politecnica de Valencia, 46022 Valencia, Spain 22 Computer Engineering Department  
proposed strategies focus on the large network environment and simply consider user satisfaction as a basic factor for caching important data. In practice, these methods are not always effective when dealing with a small limited network, such as a university network, a company network, etc. This type of network usually has a limited set of nodes, and its own data representing its direction, which is often stored in its own servers. In fact, the huge demand for content from social networks and video-on-demand (VoD) has become a large part of the data consumed in everyday life [5; 6]. This poses a problem for this type of network, since the owners of these networks will not be able to take advantage of the cached data using existing caching strategies. As an example, the analysis of data traffic at the University Amar Telidji Laghouat shows that the consumed data belonging to the university represents only 4% of the total resource consumption. In this case, with the existing strategies in the NDN, the data related to this network has no advantage in terms of delay, satisfaction, and load on the servers. This looks to be contrary to the primary goals of NDN; therefore, controlling the trade-off between maximum user satisfaction and resource efficiency is one of the most fundamental issues in a limited NDN environment. From the network administrator's perspective, it is very essential to use the network caches efficiently, and the benefits must be maximized. Similarly, from the users' perspective, it is more important that their Quality of Service (QoS) requirements are met adequately. Hence, the question is how to manage this trade-off?  
In this paper, we use a proven Bayesian classification-based machine learning method to classify the received data in such a network. Then, using the Lagrange method, we propose a utility function based on a preference parameter that allocates different partitions to the content classes in the routers. A new placement and replacement strategy is also defined for processing the data received by the routers. Finally an exhaustive simulation of the results is presented to demonstrate the performance of the proposed model.  
This paper is organized as follows. In Section 2, we review some NDN background. In Section 3, the related work on the NDN caching solution is presented. In Section 4, the proposed solution for resource allocation is described. Simulation results are then presented and discussed in Section 5. Finally, we conclude this study and discuss future research in Section 6.