Marco Luigi Della Vedova

Real-Time Modeling for Direct Load Control in Cyber-Physical Power Systems

This paper presents an innovative approach to use real-time scheduling techniques for the automation of electric loads in Cyber-Physical Power Systems. The goal is to balance the electric power usage to achieve an optimized upper bound on the power peak load, while guaranteeing specific constraints on the physical process controlled by the electric loads. Timing parameters derived from the scheduling discipline of real-time computing systems are used to model electric devices. Real-time scheduling algorithms can be exploited to achieve the upper bound by predictably and timely switching on/off the devices composing the electrical system. The paper shows the relevance of electric load balancing in power systems to motivate the use of real-time techniques to achieve predictability of electric loads scheduling. Real-Time Physical Systems (RTPS) are introduced as a novel modeling methodology of a physical system based on real-time parameters. They enable the use of traditional real-time system models and scheduling algorithms, with adequate adaptations, to manage loads activation/deactivation. The model of the physical process considered in this work is characterized by uncertainties that are compensated by a suitable feedback control policy, based on the dynamic adaptation of real-time parameter values. A number of relevant relationships between real-time and physical parameters are derived.

Electric loads as Real-Time tasks: An application of Real-Time Physical Systems

This paper describes the application of Real-Time Physical Systems (RTPS) as a novel approach to model the physical process of Cyber-Physical Systems (CPS), with specific focus on Cyber-Physical Energy Systems (CPES). The proposed approach is based on the real-time scheduling theory which is nowadays developed to manage concurrent computing tasks on processing platforms. Therefore, the physical process is modeled in terms of real-time parameters and timing constraints, so that real-time scheduling algorithms can be applied to manage the timely allocation of resources. The advantage is to leverage the strong mathematical background of real-time systems in order to achieve predictability and timing correctness on the physical process behind the considered CPS. The paper provides an introduction to the possible application of RTPS to energy systems. The analogy between real-time computing systems and energy systems is presented; moreover, the relationship between RTPS and related research fields is traced. Finally, the introduced techniques are proposed to optimize the peak load of power consumption in electric power systems. This method is suitable for systems spanning from small networks to smart grids.

Workloads in the Clouds

Despite the fast evolution of cloud computing, up to now the characterization of cloud workloads has received little attention. Nevertheless, a deep understanding of their properties and behavior is essential for an effective deployment of cloud technologies and for achieving the desired service levels. While the general principles applied to parallel and distributed systems are still valid, several peculiarities require the attention of both researchers and practitioners. The aim of this chapter is to highlight the most relevant characteristics of cloud workloads as well as identify and discuss the main issues related to their deployment and the gaps that need to be filled.

Feedback scheduling of real-time physical systems with integrator dynamics

This paper addresses the application of real-time scheduling for the reduction of the peak load of power consumption generated by electric loads in a power system. The considered physical processes are characterized by integrator dynamics and modeled as sporadic real-time activities. To enable the applicability in realistic scenarios, modeling approximations and uncertainties on physical parameters are explicitly included in the model. A feedback control strategy is proposed to guarantee the requirements on physical values under control in presence of modeling and measurement uncertainties. To compensate for such uncertainties, the value of timing parameters used by the scheduler are dynamically adapted. Formal results have been derived to put into relationship the values of quantities describing the physical process with real-time parameters used to model and to schedule the activation of loads.

Probabilistic provisioning and scheduling in uncertain Cloud environments

Resource provisioning and task scheduling in Cloud environments are quite challenging because of the fluctuating workload patterns and of the unpredictable behaviors and unstable performance of the infrastructure. It is therefore important to properly master the uncertainties associated with Cloud workloads and infrastructure. In this paper, we propose a probabilistic approach for resource provisioning and task scheduling that allows users to estimate in advance, i.e., offline, the resources to be provisioned, thus reducing the risk and the impact of overprovisioning or underprovisioning. In particular, we formulate an optimization problem whose objective is to identify scheduling plans that minimize the overall monetary cost for leasing Cloud resources subject to some workload constraints. This cost-aware model ensures that the execution time of an application does not exceed with a given probability a specified deadline, even in presence of uncertainties. To evaluate the behavior and sensitivity to uncertainties of the proposed approach, we simulate a simple batch workload consisting of MapReduce jobs. The experimental results show that, despite the provisioning and scheduling approaches that do not take into account the uncertainties in their decision process, our probabilistic approach nicely adapts to workload and Cloud uncertainties.

Platooning control of autonomous nonholonomic mobile robots in a human-robot coexisting environment

This paper proposes a gradient tracking algorithm based on artificial harmonic potential fields, to support the platooning of a team of nonholonomic mobile robots. The main motivation is the need of dynamically changing the goal-point associated with each mobile robot, in order to guarantee the platoon string stability. Mobile obstacles are taken into account with an approach based on the so-called collision cone, and a time-varying artificial security radius is associated with each obstacle, in order to prevent collisions. In addition, the proposed method ensures recovering of the connectivity between robots forming the platoon, after that one of them goes far away from the others and loses the connection during an obstacle avoidance maneuver. Finally, the so-called interference index has been evaluated, to show the low impact of robot motion on human behaviors.

Visual Interaction for Real-Time Navigation of Autonomous Mobile Robots

Visual feedback is one of the most adopted solutions for driving the navigation of autonomous robots in unknown environments. This paper presents the structure of a visual interaction system suitable for real-time robotics applications. By means of a specific modeling, the visual system allows a team of mobile robots to perform any relevant visual task in a timely fashion. As a matter of fact, the guarantee of real-time constraints for the processing tasks related with the visual feedback is crucial to achieve an accurate and robust control of mobile robots. The proposed visual infrastructure is based on a single camera, which provides a global view of the robots workspace. A degenerated camera model is developed to allow a planar motion in R3 . The model simplifies the visual system calibration, while reducing the cost of coordinates transforms between the real-world and the image space during the system operation. To show the behaviour and to derive the performances of the visual interaction system, experimental results are carried out considering the real-time navigation of autonomous mobile robots.

Real-time platooning of mobile robots: design and implementation

The platooning is a coordination technique for teams of mobile units that aims at letting each unit to move closely to its preceding neighbour, thus forming the so-called platoon. This paper describes the design and implementation of a distributed robotics application where a team of autonomous mobile robots are coordinated to move as a platoon. The focus will be on the on-board real-time computing that allows a predictable robots behavior. Experimental results are shown to assess the performance of the proposed platform.

The Economics of Cloud Parallelism under Uncertainty

Parallelism has many advantages in accelerating compute tasks amenable to speed up. However, whereas parallel processing in an elastic, pay-per-use cloud can generate numerous benefits, theres a hidden downside due to the fundamental statistics and interrelationships of tasks whose completion times are stochastic.

Applying limited-preemptive scheduling to peak load reduction in smart buildings

The coordination of appliances in a smart building to limit the peak load is one of the common objectives of power load management approaches such as the Demand-Side Management (DSM). The DSM, in turn, is an important research challenge in the field of smart energy systems and smart grids. This paper investigates the use of the limited-preemption scheduling approach to the coordination of a set of household appliances in a smart building. This approach is enabled by the application of a real-time scheduling framework to manage the activation of electric loads. The limited-preemption technique aims to reduce the number of stop/restart operations applied to interruptible devices, while ensuring the same performance in terms of peak load reduction. The original contribution of this paper w.r.t. to previous works on real-time scheduling with limited-preemption is to present some peculiar issues related to preemptions of electric loads and to assess suitability and benefits of this approach when applied to interruptible household appliances. Simulated results show the effectiveness of this method.