Mauro Gaggero

Modeling and Feedback Control for Resource Allocation and Performance Analysis in Container Terminals

A dynamic discrete-time model of container flows in maritime terminals is proposed as a system of queues. Such queues are controlled via input variables that account for the use of the available resources given by the capacities of the handling machines used to move containers inside a terminal. Two feedback control strategies for the allocation of such resources are described. The first consists of a resource assignment that is proportional to the corresponding queue lengths; in the second, the assignment is obtained by the one-step-ahead optimization of a performance cost function according to a myopic approach. Simulation results are reported to compare such methodologies for the purpose of sensitivity and scenario analyses in the management of a maritime terminal.

Moving-Horizon State Estimation for Nonlinear Systems Using Neural Networks

In recent results, a moving-horizon state estimation problem has been addressed for a class of nonlinear discrete-time systems with bounded noises acting on the system and measurement equations. For the resulting estimator, suboptimal solutions can be addressed for which a certain error is allowed in the minimization of the cost function. Building on such results, in this paper the use of nonlinear parameterized functions is studied to obtain suitable state estimators with guaranteed performance. Thanks to the off-line optimization of the parameters, the estimates can be generated on line almost instantly. A new technique based on the approximation of the cost value (and not of its argument) is proposed and the properties of such a scheme are studied. Simulation results are presented to show the effectiveness of the proposed approach in comparison with the extended Kalman filter.

Feedback Optimal Control of Distributed Parameter Systems by Using Finite-Dimensional Approximation Schemes

Optimal control for systems described by partial differential equations is investigated by proposing a methodology to design feedback controllers in approximate form. The approximation stems from constraining the control law to take on a fixed structure, where a finite number of free parameters can be suitably chosen. The original infinite-dimensional optimization problem is then reduced to a mathematical programming one of finite dimension that consists in optimizing the parameters. The solution of such a problem is performed by using sequential quadratic programming. Linear combinations of fixed and parameterized basis functions are used as the structure for the control law, thus giving rise to two different finite-dimensional approximation schemes. The proposed paradigm is general since it allows one to treat problems with distributed and boundary controls within the same approximation framework. It can be applied to systems described by either linear or nonlinear elliptic, parabolic, and hyperbolic equations in arbitrary multidimensional domains. Simulation results obtained in two case studies show the potentials of the proposed approach as compared with dynamic programming.

Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results

Value-function approximation is investigated for the solution via Dynamic Programming (DP) of continuous-state sequential N-stage decision problems, in which the reward to be maximized has an additive structure over a finite number of stages. Conditions that guarantee smoothness properties of the value function at each stage are derived. These properties are exploited to approximate such functions by means of certain nonlinear approximation schemes, which include splines of suitable order and Gaussian radial-basis networks with variable centers and widths. The accuracies of suboptimal solutions obtained by combining DP with these approximation tools are estimated. The results provide insights into the successful performances appeared in the literature about the use of value-function approximators in DP. The theoretical analysis is applied to a problem of optimal consumption, with simulation results illustrating the use of the proposed solution methodology. Numerical comparisons with classical linear approximators are presented.

Seeing the Unseen: Revealing Mobile Malware Hidden Communications via Energy Consumption and Artificial Intelligence

Modern malware uses advanced techniques to hide from static and dynamic analysis tools. To achieve stealthiness when attacking a mobile device, an effective approach is the use of a covert channel built by two colluding applications to exchange data locally. Since this process is tightly coupled with the used hiding method, its detection is a challenging task, also worsened by the very low transmission rates. As a consequence, it is important to investigate how to reveal the presence of malicious software using general indicators, such as the energy consumed by the device. In this perspective, this paper aims to spot malware covertly exchanging data using two detection methods based on artificial intelligence tools, such as neural networks and decision trees. To verify their effectiveness, seven covert channels have been implemented and tested over a measurement framework using Android devices. Experimental results show the feasibility and effectiveness of the proposed approach to detect the hidden data exchange between colluding applications.

Efficient kernel models for learning and approximate minimization problems

This paper investigates techniques for reducing the computational burden of local learning methods relying on kernel functions in the framework of approximate minimization, i.e., when they are employed to find the minimum of a given cost functional. The considered approach is based on an optimal choice of the kernel width parameters through the minimization of an empirical cost and can provide a solution to important problems, such as function approximation and multistage optimization. However, when the stored data are too many, the kernel model output evaluation can take a long time, making local learning unsuited to contexts where a fast function evaluation is required. At the same time, the training procedure to obtain the kernel widths can become too demanding as well. Here it is shown that a large saving in the computational effort can be achieved by considering subsets of the available data suitably chosen according to different criteria. An analysis of the performance of the new approach is provided. Then, simulation results show in practice the effectiveness of the proposed techniques when applied to learning and approximate minimization problems.

Low-discrepancy sampling for approximate dynamic programming with local approximators

Approximate dynamic programming (ADP) relies, in the continuous-state case, on both a flexible class of models for the approximation of the value functions and a smart sampling of the state space for the numerical solution of the recursive Bellman equations. In this paper, low-discrepancy sequences, commonly employed for number-theoretic methods, are investigated as a sampling scheme in the ADP context when local models, such as the Nadaraya-Watson (NW) ones, are employed for the approximation of the value function. The analysis is carried out both from a theoretical and a practical point of view. In particular, it is shown that the combined use of low-discrepancy sequences and NW models enables the convergence of the ADP procedure. Then, the regular structure of the low-discrepancy sampling is exploited to derive a method for automatic selection of the bandwidth of NW models, which yields a significant saving in the computational effort with respect to the standard cross validation approach. Simulation results concerning an inventory management problem are presented to show the effectiveness of the proposed techniques.

Predictive Control of Container Flows in Maritime Intermodal Terminals

Predictive control is investigated as a paradigm for the allocation of handling resources to transfer containers inside intermodal terminals. The decisions on the allocation of such resources are derived from the minimization of performance cost functions that measure the lay times of carriers over a forward horizon basing on a model of the container flows. Such a model allows one to take advantage of the information available in real time on the arrival or departure of carriers with the corresponding amounts of containers scheduled for loading or unloading. The resulting strategy of resource allocation can be regarded as a feedback control law and is obtained by solving nonlinear programming problems online. Since the computation may be too expensive, a technique based on the idea of approximating offline such a law is proposed. The approximation is performed by using neural networks, which allow one to construct an approximate feedback controller and generate the corresponding online control actions with a negligible computational burden. The effectiveness of the approach is shown via simulations in a case study.

Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure

Existence, uniqueness, and approximations of smooth solutions to team optimization problems with stochastic information structure are investigated. Suboptimal strategies made up of linear combinations of basis functions containing adjustable parameters are considered. Estimates of their accuracies are derived by combining properties of the unknown optimal strategies with tools from nonlinear approximation theory. The estimates are obtained for basis functions corresponding to sinusoids with variable frequencies and phases, Gaussians with variable centers and widths, and sigmoidal ridge functions. The theoretical results are applied to a problem of optimal production in a multidivisional firm, for which numerical simulations are presented.

Predictive Control for Energy-Aware Consolidation in Cloud Datacenters

Infrastructure-as-a-Service is one of the most used paradigms of cloud computing and relies on large-scale datacenters with thousands of nodes. As a consequence of this success, the energetic demand of the infrastructure may lead to relevant economical costs and environmental footprint. Thus, the search for power optimization is of primary importance. In this perspective, this paper introduces an energy-aware consolidation strategy based on predictive control, in which virtual machines are properly migrated among physical machines to reduce the amount of active units. To this aim, a discrete-time dynamic model and suitable constraints are introduced to describe the cloud. The migration strategies are obtained by solving finite-horizon optimal control problems involving integer variables. The proposed method allows one to trade among power savings and violations of the service level agreement. To prove its effectiveness, a simulation campaign is conducted in different scenarios using both synthetic and real workloads, also by performing a comparison with three heuristics selected from the reference literature.