Placing virtual machines so to minimize the number of used physical hosts is an utterly important problem in cloud computing and next-generation virtualized networks.This article proposes a declarative reasoning methodology, and its open-source prototype, including four heuristic strategies to tackle this problem.Our proposal is extensively assessed over real data from an industrial case study and compared to state-of-the-art approaches, both in terms of execution times and solution optimality.As a result, our declarative approach determines placements that are only 6% far from optimal, outperforming a state-of-the-art genetic algorithm in terms of execution times, and a first-fit search for optimality of found placements.Last, its pipelining with a mathematical programming solution improves execution times of the latter by one order of magnitude on average, compared to using a genetic algorithm as a primer.

This paper constitutes an industrial experience re-port about the use of data center optimization strategies for softwarized network services within the Vodafone resource man-agement unit for the management of virtualized network infras-tructures. The problem of optimum virtual machine placement as needed in the network operator context is detailed, and different solving strategies are proposed and discussed, including heuristics based on genetic optimization. Also, experimental results are presented that compare these strategies with one another from the standpoint of optimality and execution times, using a data-set made of some of the real problems that had to be solved in the past few years by Vodafone, in order to optimize its capacity planning decisions. The presented experimental results highlight that an optimum solver leads to excessively high computation times for large problems, whereas simple heuristics may exhibit significant loss in optimality at reduced computation times. Genetic optimization, on the other hand, constitutes a very interesting trade-off between these two extremes. The data-set used for the provided results is published under an open data license, for possible reuse in future research works on the topic.

In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By focusing on feedback linearizable systems, we show how to combine expert demonstrations into a stabilizing controller, provided that demonstrations are sufficiently long and there are at least$n+1$of them, where$n$is the number of states of the system being controlled. The results are experimentally demonstrated on a CrazyFlie 2.0 quadrotor.

Supervision of a nominal controller, to enforce safety, is concerned with appropriately modifying the generated control inputs, if needed, in order to keep a control system within a set of safe states. An integral component in supervision is a controlled invariant set contained in the set of safe states. In this paper, we build on recent results on the computation of polytopic controlled invariant sets to present a supervision framework that computes the corrected inputs analytically and, hence, suitable for real-time control. The framework is validated on the task of quadrotor obstacle avoidance by forcing the vehicle to navigate within controlled invariant sets of the obstacle-free space. The results are experimentally demonstrated on a Crazyflie 2.0 quadrotor.

Most works in schedulability analysis theory are based on the assumption that constraints on the performance of the application can be expressed by a very limited set of timing constraints (often simply hard deadlines) on a task model. This model is insufficient to represent a large number of systems in which deadlines can be missed, or in which late task responses affect the performance, but not the correctness of the application. For systems with a possible temporary overload, models like the m-K deadline have been proposed in the past. However, the m-K model has several limitations since it does not consider the state of the system and is largely unaware of the way in which the performance is affected by deadline misses (except for critical failures). In this paper, we present a state-based representation of the evolution of a system with respect to each deadline hit or miss event. Our representation is much more general (while hopefully concise enough) to represent the evolution in time of the performance of time-sensitive systems with possible time overloads. We provide the theoretical foundations for our model and also show an application to a simple system to give examples of the state representations and their use.