Software Defined Networking (SDN) has been identified as a potential approach to achieve a more flexible control and management of the traditional satellite systems and enhance the opportunities for future services including the possibility of a hybrid satellite/terrestrial network. Given the renewed interest towards Low-Earth-Orbit (LEO) constellations, an interesting research topic is the design of a suitable network management model taking into account user specific metrics. In this paper, we address this issue while investigating the use-case scenario of an SDN-enabled satellite space segment. A Dynamic Controller Placement Problem (DCPP) is considered for a LEO constellation where the traffic demands change dynamically based on users' geographical position and time zone. To this end, we develop a mathematical model and formulate it as an Integer Linear Programming (ILP) guaranteeing an optimal controller placement and satellite-to-controller assignment minimizing the average flow setup time with respect to the traffic dynamics. We show results for the DCPP regarding the average flow setup time. Furthermore, a comparison with respect to the static approach is investigated and the proposed SDN-enabled LEO constellation architecture is compared with alternative architectures proposed in the state of the art.
Network slicing is envisioned as a tool for 5G networks to provide network flexibility and isolation among different logical networks. While network slicing is well investigated in the fixed-network side, in the Radio Access Network (RAN) there remain challenging problems, which originate mainly from the stochastic nature of the wireless channels and complex resource coupling between slices. In this work, we investigate a network slicing problem for the downlink RAN of a cellular network. Our target is the reduction of resource usage while guaranteeing slice isolation and simultaneously accounting for each slice's average rate and delay requirements. We tackle the problem with a Lyapunov optimization approach, leading to a simple resource assignment procedure that we can prove to achieve isolation while satisfying all slice guarantees. The proposed procedure leads to a functional split, where resources are scheduled within each slice by a slice manager, while a Software-Defined RAN (SD-RAN) controller dynamically reassigns resources to each slice. We verify our approach through extensive simulations and provide insight on how to fine-tune available system parameters.
In the context of the 5G ecosystem, the integration between the terrestrial and satellite networks is envisioned as a potential approach to further enhance the network capabilities. In light of this integration, the satellite community is revisiting its role in the next generation 5G networks. Emerging technologies such as Software-Defined Networking (SDN) which rely on programmable and reconfigurable concepts, are foreseen to play a major role in this regard. Therefore, an interesting research topic is the introduction of management architecture solutions for future satellite networks driven by means of SDN. This anticipates the separation of the data layer from the control layer of the traditional satellite networks, where the control logic is placed on programmable SDN controllers within traditional satellite devices. While a centralized control layer promises delay reductions, it introduces additional overheads due to reconfiguration and migration costs. In this paper, we propose a method to quantify the overhead imposed on the network by the aforementioned parameters while investigating the usecase scenario of an SDN-enabled satellite space segment. We make use of an optimal controller placement and satellite-tocontroller assignment which minimizes the average flow setup time with respect to varying traffic demands. Furthermore, we provide insights on the network performance with respect to the migration and reconfiguration cost for our proposed SDNenabled architecture. Finally, we compare our proposed space segment SDN-enabled architecture with alternative solutions in the state-of-the-art given the aforementioned performance metrics.
Network programmability also sneaked into the mobile world leading to the emergence of Software-Defined Radio Access Network (SD-RAN) architectures. Interestingly, while only a small number of prototype architectures exist for SD-RAN, their performance evaluations are unfortunately also limited. Recent evaluations are carried out for small network dimensions of up to 50 devices, while emerging 5G/6G networks envision numbers of devices beyond 5000. Although 5G/6G applications are more stringent with respect to latency guarantees, performance evaluations of such low scale remain questionable. To fill this void, this paper presents MARC: a novel benchmarking tool for SD-RAN architectures and their controllers. We use MARC to measure, analyze and identify performance implications for two state-of-the-art open source SD-RAN solutions: FlexRAN and 5G-EmPOWER. We perceive results for monitoring application scenarios considering fully centralized control. For this setting, our findings show that the proposed architectures with a single SD-RAN controller are not scalable and can even lead to unpredictable network operations. Using our tool and based on our insights, we provide and implement design guidelines for the internal working behavior of the existing controllers.
Age-of-information (AoI) is a metric quantifying information freshness at the receiver. It has received a lot of attention as it captures the delay together with packet loss and packet generation rate. However, most of the work aim for the average or peak AoI neglecting the complete distribution. In this work, we consider a n-hop network between a source and destination pair with time-invariant packet loss probabilities on each link. We derive closed form equations for the probability mass function of AoI at the receiver. We verify our findings with simulations. Our results show that the performance indicators considered in the literature such as average AoI or peak AoI may give misleading insights into the complete AoI performance.
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