Network Function Virtualization (NFV) has already proven its efficiency to deploy networking services in large-scale. Recent advances of cloud-native applications may bring new advantage by deploying and implementing Virtual Network Function (VNFs) as cloud-native Containers rather than virtual machines. Beside remarkable advantages such as lower overhead and faster running, microservices (cloud-native containers) intend to save costs while increasing the service agility. To this end, in this paper we extend consolidated state-of-the-art tools and technologies developed in two domains cloud-native applications and Network Function Virtualization (NFV). The proposed framework chains services provisioned across Kubernetes and Contiv/VPP domains and using containers. Our orchestration framework chain services across distributed CNFs. Furthermore, we propose K -TS scheme to load balance the traffic over services replicas. K -TS is based on Ketama Consistent hashing algorithm. Experimental simulations show very good results for both the service chaining framework in term of QoS satisfaction such as: packet error rate, throughput satisfaction and jitter.

Matching heterogeneous text documents coming from different sources means matching data extracted from these documents, generally structured in the form of vectors. The accuracy of matching directly depends on the right choice of the content of these vectors. That's why we need to select the best features. In this paper, we present a new approach to select the minimum set of features that represents the semantics of a set of text documents, using a quantum inspired genetic algorithm. Among different Vs characterizing the big data we focus on 'Variety' criterion, therefore, we used three sets of different sources that are semantically similar to retrieve their best features which describe the semantics of the corpus. In the matching phase, our approach shows significant improvement compared with the classic 'Bag-of-words' approach.