Internal Model Hop-by-hop Congestion Control for High-Speed Networks

This paper presents a hop-by-hop congestion control for highspeed networks. The control policy relies on the data exchange between adjacent nodes of the network (nearest-neighbour interaction). The novelty of this paper consists in the extensive use of Internal Model Control (IMC) to set the rates of the traffic flows. As a result, the proposed congestion control provides upper-bounds of the queue lengths in all the network buffers (overflow avoidance), avoids wasting the assigned capacity (full link utilisation) and guarantees the congestion recovery. Numerical simulations prove the effectiveness of the scheme

Wardrop Equilibrium in Discrete-Time Selfish Routing with Time-Varying Bounded Delays

This paper presents a multi-commodity, discrete- time, distributed and non-cooperative routing algorithm, which is proved to converge to an equilibrium in the presence of heterogeneous, unknown, time-varying but bounded delays. Under mild assumptions on the latency functions which describe the cost associated to the network paths, two algorithms are proposed: the former assumes that each commodity relies only on measurements of the latencies associated to its own paths; the latter assumes that each commodity has (at least indirectly) access to the measures of the latencies of all the network paths. Both algorithms are proven to drive the system state to an invariant set which approximates and contains the Wardrop equilibrium, defined as a network state in which no traffic flow over the network paths can improve its routing unilaterally, with the latter achieving a better reconstruction of the Wardrop equilibrium. Numerical simulations show the effectiveness of the proposed approach

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches

Robust Adaptive Congestion Control for Next Generation Networks

This paper deals with the problem of congestion control in a next-generation heterogeneous network scenario. The algorithm runs in the 'edge' routers (the routers collecting the traffic between two different networks) with the aim of avoiding congestion in both the network and the edge routers. The proposed algorithm extends congestion control algorithms based on the Smith's principle: i) the controller, by exploiting on-line estimates via probe packets, adapts to the delay and rate variations; ii) the controller assures robust stability in the presence of time-varying delays

Control-Based Resource Management Procedures for Satellite Networks

This paper describes the resource management of a DVBRCS geostationary satellite network. The functional modules of the access layer aim at efficiently exploiting the link resources while assuring the contracted Quality of Service (QoS) to the traffic entering the satellite network. The main novelty is the integration between the Connection Admission Control and the Congestion Control procedures. Both them exploit the estimation of the traffic load, performed by a Kalman filter. The proposed solution has been analysed via computer simulations, which confirmed their effectiveness

Efficient and Risk-Aware Control of Electricity Distribution Grids

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers. The core of the EMPC algorithm is a nonconvex optimization problem integrating the ESSs dynamics, the topological and power technical constraints of the grid, and the modeling of the cascading effects of potential adverse events. An equivalent (i.e., having the same optimal solution) proxy of the nonconvex problem is proposed to make the solution more tractable. Simulations performed on a 16-bus test distribution network validate the proposed control strategy

Distributed MARL with Limited Sensing for Robot Navigation Problems

This paper proposes a Multi-Agent Reinforcement Learning (MARL) algorithm for the multi-robot navigation problem. Most of the proposals in the literature requires some form of information sharing and communications among agents to coordinate their action in order to complete the overall task. The proposed paper, named Limited Sensing MARL (LS-MARL), assumes that each robot decisions rely on local information and is provided with sensor, which can be switched on for the localization of the robots within a given range. Besides the navigation task, each agent aims at limiting the use of the sensor as much as possible (i.e., to be as independent as possible) for energy saving or safety reasons. The algorithm is evaluated by simulations and favourably compares to the one proposed in (Yu et al. (2015)), that assumes a similar setup in which the neighbouring agents share their positioning information

A Load Balancing Algorithm for Equalising Latency across Fog or Edge Computing Nodes

Abstract: When dealing with distributed applications in Edge or Fog computing environments, the service latency that the user experiences at a given node can be considered an indicator of how much the node itself is loaded with respect to the others. Indeed, only considering the average CPU time or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters are application- and hardware-agnostic. They do not give any information about how the application is performing from the user's perspective, and they cannot be used for a QoS-oriented load balancing. In this article, we propose a load balancing algorithm that is focused on the service latency with the objective of levelling it across all the nodes in a fully decentralised manner. In this way, no user will experience a worse QoS than the other. By providing a differential model of the system and an adaptive heuristic to find the solution to the problem in real settings, we show both in simulation and in a real-world deployment, based on a cluster of Raspberry Pi boards, that our approach is able to level the service latency among a set of heterogeneous nodes organised in different topologies

Multi-agent quality of experience control

In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalities is to track the personalized desired QoE level of the applications. The paper proposes to perform such a task by dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), this selection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The paper shows that such an approach offers the opportunity to cope with some practical implementation problems: in particular, it allows to face the so-called “curse of dimensionality” of MARL algorithms, thus achieving satisfactory performance results even in the presence of several hundreds of Agents

Gateway Architecture for DVB-RCS Satellite Networks

n/