A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 1) SDN <s> A hybrid software defined networks (SDN) network contains both traditional and SDN network, which combines the robustness of traditional protocols with the flexibility of SDN while avoiding their limitations and incompatibility. However, a hybrid SDN network comes with its own set of challenges, including error-prone deployment processes, risks of inconsistency, and complex incremental deployment strategies. In this paper, we present a survey of the deployment solutions and optimization strategies for hybrid SDN networks. We systematically review solutions to control plane and data plane deployments, and describe typical use cases of hybrid SDN networks. We discuss and compare various optimization strategies from perspectives of traffic engineering, resource saving, network control capacity, and network security. This paper aims to provide insights to researchers into the future development of hybrid SDN networks and inspire more efforts in this area. <s> BIB001 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 1) SDN <s> Due to copyright restrictions full text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article). <s> BIB002 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 1) SDN <s> An intelligent transportation system (ITS) is a cyber physical system (CPS) that is well recognized due to its distinctive applications such as passenger safety, enhanced traffic efficiency, and infotainment services. However, very recently, these CPSs have witnessed a rapid increase in the number of autonomous vehicles (AVs), and the count is expected to increase significantly in the years to come. Under such scenarios, the conventional ITS solutions would fall short due to numerous technical limitations such as reduced flexibility, poor connectivity, limited scalability, and lack of adequate intelligence. In this direction, the cloud computing paradigm would also lag behind in meeting the following expected challenges: high mobility, minimal latency, realtime services, and high quality of service (QoS). Software defined networking (SDN) and edge computing (EC) are expected to be promising solutions for modern ITS. SDN allows efficient management of networks by virtue of its decoupled data and control planes. Moreover, EC is an extension of cloud computing that allows data processing, storage, and management at the edge of the network. Thus, these promising paradigms can address the network and data processing challenges of AV networks in rea time. Further, distributed mobility management (DMM) with improvised routing capabilities in the considered setup can bring added advantages in the form of zero tunneling overhead, full-fledged control of flow table entries, QoS-driven routing decisions, and efficient interplay between the edge and the cloud. Keeping in view the potential benefits of SDN, EC, and DMM in future AV networks, this work presents a composite framework with a distributed SDN-DMM approach in ITS ecosystems. Further, conventional DMM has been improved by stacking the Optimized Routing Decision module on it. This coupling yields a mobility-aware and QoS-driven (MobQoS) SDN framework for AVs that handles both the mobility and QoS challenges of the underlying vehicular networks. Additionally, it is also designed to handle the interplay between the edge and the cloud in an optimized manner using the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). Numerical comparisons depict that the proposed MobQoS outperforms the existing state of the art in terms of overall communication latency and bandwidth utilization. The performance assessment has been carried out using NS-3 and SUMO wherein an overall improvement of 15.95 percent has been reported in terms of end-to-end delay. <s> BIB003
As an emerging network architecture, SDN has the distinct advantage of separating the control plane and data plane BIB001 , resulting in flexible and efficient usage of network resources. It has found partial commercial applications and been tested extensively. However, it still faces many challenges in several aspects before its practical large-scale deployment, such as the software and hardware implementations in the SDN-based data plane, control plane, management plane and application plane. Some of the most important issues are the network planning for network elements designer, and the resource allocation algorithms for the operators in the scenario where there are hundreds of billions of terminals in the emerging Internet of Things. In the case of constrained resources, the performance metric or multiobjective of the routing algorithm will be diversionary, such as the minimum objectives: capital expenditures (Capex), operating expenses (Opex), deployment cost, energy consumption BIB001 , and end to end delay BIB002 . The maximum objectives are operators' profits, differentiated service levels, quality of experiences, etc. For example, S. Garg et al. BIB003 addressed an SDN framework for autonomous vehicles by using MOEA/D, aiming at minimizing the network latency and maximizing bandwidth utilization. The architecture of numerous SDN controllers is reasonably a multi-layered tree since it matches the structure of our community well. We could use MOEA/D variants  to partition such huge terminals into thousands of autonomous domains. Such MOEA/D variants mentioned in this article could be used in one domain to optimize the above performance metrics which are diversionary or in conflict with each other. However, for the whole network, we may need to employ the multi-agent optimization architecture in which each of all MOEA/D optimizers or agents works with other ones together, exploring the global optimization.