On the optimization of energy storage system placement for protecting power transmission grids against dynamic load altering attacks

In this paper a power system protection scheme based on energy storage system placement against closed-loop dynamic load altering attacks is proposed. The protection design consists in formulating a non-convex optimization problem, subject to a Lyapunov stability constraint and solved using a two-step iterative procedure. Simulation results confirm the effectiveness of the approach and the potential relevance of using energy storage systems in support of primary frequency regulation services

A distributed load balancing algorithm for the control plane in software defined networking

The increasing demand of bandwidth, low latency and reliability, even in mobile scenarios, has pushed the evolution of the networking technologies in order to satisfy the requirements of innovative services. In this context, Software Defined Networking (SDN), namely a new networking paradigm that proposes the decoupling of the control plane from the forwarding plane, enables network control centralization and automation of the network management. In order to address the performance issues related to the SDN Control Plane, this paper proposes a distributed load balancing algorithm with the aim of dynamically balancing the control traffic across a cluster of SDN Controllers, thus minimizing the latency and increasing the overall cluster throughput. The algorithm is based on game theory and converges to a specific equilibrium known as Wardrop equilibrium. Numerical simulations show that the proposed algorithm outperforms a standard static configuration approach

SDN workload balancing and QoE control in next generation network infrastructures

The increasing demand of bandwidth, low latency and reliability, even in mobile scenarios, has pushed the evolution of the networking technologies to satisfy new requirements of innovative services. Flexible orchestration of network resources is increasingly being investigated by the research community and by the service operator companies as a mean to easily deploy new remunerative services while reducing capital expenditures and operating expenses. In this regard, the Future Internet initiatives are expected to improve state of the art technologies by developing new orchestrating platforms based on the most prominent enabling technologies, namely, Software Defined Network (SDN) orchestrated Network Function Virtualization (NFV) infrastructure. After introducing the fundamental of the Next Generation Network, formalized as the conceptual Future Internet Platform architecture, the reference scenarios and the proposed control frameworks are given. The thesis discusses the design of two resources management framework of such architecture, targeted, respectively, (i) at the balancing of SDN Control traffic at the network core and (ii) at the user Quality of Experience (QoE) evaluation and control at the network edge. Regarding the first framework, to address the issues related with the adoption of a logically centralized but physically distributed SDN control plane, a discrete-time, distributed, non-cooperative load balancing algorithm is proposed, based on game theory and converged to a specific equilibrium known as Wardrop equilibrium. Regarding the QoE framework, a cognitive approach is presented, aimed at controlling the Quality of Experience (QoE) of the end users by closing the loop between the provided QoS and the user experience feedbacks parameters. QoE Management functionalities are aimed at approaching the desired QoE level exploiting a mathematical model and methodology to identify a set of QoE profiles and an optimal and adaptive control strategy based on a Reinforcement Learning algorithm. For both the proposed solutions, simulation and proof-of-concept implementation results are presented and discussed, to highlight the correctness and the effectiveness of the proposed solutions