The coexistence of various protocols for current network equipment leads to extremely complex network systems, which not only limit the development of network technologies, but also cannot meet the growing demands for cloud computing, big data, and service visualization applications, just to name a few. As a new network architecture, parallel networks are expected to revolutionize this situation and meet the evolving demands for network services. The main idea of a parallel network is to leverage upon software-defined networking to construct artificial networks, and then effectively optimize the network system operations via the interactions between actual and artificial networks. The foundation of the parallel network is the theory of ACP, composed of artificial societies, computational experiments, and parallel execution. By the computational experiments and analysis of the artificial network, a control strategy based on network traffic flow can be continuously updated and tracked on a real-time basis; meanwhile, the collected operating status of the actual network can also be used to optimize the model of the artificial network. These strategies can be applied to all types of network equipment to control network operations at various levels; thus, it is possible to allocate the network resources more effectively, improve the management and utilization of resources, and then provide new network solutions to effectively address the constantly evolving network demands for network performance, scalability, and security.
Bibliographical noteFunding Information:
This work is partly supported by the National Natural Science Foundation of China (Grant no. 61571020, 61172105, 61533019, 71232006, 61233001, 71402178, 71472174, 61501461 and 61471269); by National Science Foundation Grant no. CNS-1343189; by the National 973 Project under Grant 2013CB336700; by the National 863 Project under Grants 2014AA01A706 and SS2015AA011306; by the Major Project from Beijing Municipal Science and Technology Commission under Grant D151100000115004; and by the Early Career Development Award of SKLMCCS (Y3S9021F34).
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