F.3.3. Graph Theor
A. Jalili; M. Keshtgari
Abstract
Software-Defined Network (SDNs) is a decoupled architecture that enables administrators to build a customizable and manageable network. Although the decoupled control plane provides flexible management and facilitates the task of operating the network, it is the vulnerable point of failure in SDN. To ...
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Software-Defined Network (SDNs) is a decoupled architecture that enables administrators to build a customizable and manageable network. Although the decoupled control plane provides flexible management and facilitates the task of operating the network, it is the vulnerable point of failure in SDN. To achieve a reliable control plane, multiple controller are often needed so that each switch must be assigned to more than one controller. In this paper, a Reliable Controller Placement Problem Model (RCPPM) is proposed to solve such a problem, so as to maximize the reliability of software defined networks. Unlike previous works that only consider latencies parameters, the new model takes into account the load of control traffic and reliability metrics as well. Furthermore, a near-optimal algorithm is proposed to solve the NP-hard RCPPM in a heuristic manner. Finally, through extensive simulation, a comprehensive analysis of the RCPPM is presented for various topologies extracted from Internet Topology Zoo. Our performance evaluations show the efficiency of the proposed framework.
F.2.7. Optimization
F. Tatari; M. B. Naghibi-Sistani
Abstract
In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information. Detailed analysis of online optimal leader-follower consensus under known and unknown dynamics is presented. ...
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In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information. Detailed analysis of online optimal leader-follower consensus under known and unknown dynamics is presented. The introduced reinforcement learning-based algorithms learn online the approximate solution to algebraic Riccati equations. An optimal adaptive control technique is employed to iteratively solve the algebraic Riccati equation based on the online measured error state and input information for each agent without requiring the priori knowledge of the system matrices. The decoupling of the multi-agent system global error dynamics facilitates the employment of policy iteration and optimal adaptive control techniques to solve the leader-follower consensus problem under known and unknown dynamics. Simulation results verify the effectiveness of the proposed methods.
