As power grids transition into decentralized Cyber-Physical Power Systems, the digitalization of legacy devices has dramatically multiplied the potential entry points for cyber adversaries. To accurately assess these threats, security models must capture both detailed attack steps and network timing effects. However, obtaining real-world cyber incident data to parameterize these models is rarely possible due to strict privacy and confidentiality constraints within utility companies. To overcome this limitation, we present a simulation-based framework that integrates Attack Graphs (AGs) with the OMNeT++ Discrete Event Simulation environment and Dynamic Bayesian Networks (DBNs). Attack step logic is modeled through Control Finite State Machines, allowing for dynamic interactions between attacker behavior and simulated network components. The simulation automatically generates time-series data used to train AG-derived DBNs used to answer probabilistic queries on attack progression. We present a case study on a Distributed Energy Resource cyberattack which demonstrates that the framework captures complex temporal dependencies and maintains high predictive accuracy supporting cybersecurity assessment for critical energy infrastructures. The code and models implemented in this work are available here: https://github.com/Dosclic98/dbn-sim-learning.git.

As embedded and mobile systems grow pervasive in people lives and expand their reach, paradigms related to mobile crowdsensing (MCS) are going to play an ever more prominent role. Innovative methodologies and applications able to unlock such a huge potential are required. A domain where MCS really fits is mobility and transportation. However, for a full exploitation of this paradigm in intelligent transportation systems (ITS), distributed and self-management capabilities for the involved nodes-vehicles have to be provided. This paper is a first step in this direction, laying out an optimization system by exploiting feedback-driven patterns in a distributed-opportunistic way. In this sense, collective intelligence and stigmergic, swarm-based paradigms are adapted to an innovative decentralized MCS pattern toward new approaches in ITS. Their effectiveness is demonstrated through a traffic engineering case study, where route planning services are designed according to the proposed approach and then modeled and evaluated by Markovian agents.

Summary The capabilities of multicore processors lead them to be widely adopted in systems at any scale, since their are able to provide more computing power at a lower consumption and dissipation cost. System designers are challenged to a deeper understanding of multicore functioning in order to fully exploit them while keeping the optimal balance between cores utilization and optimal throughput, response time and energy usage. Besides the advancement of general purpose CPUs, the same technological evolution leads to the rise of GPUs, dramatic evolution of graphical coprocessors, that are now affordable, efficient, dedicated computing units, capable of parallel computing and equipped with facilities that make them suited for supporting the main CPU of a system in running ordinary applications. The availability of commercial off‐the‐shelf (COTS) multicore computers, eventually equipped with one or more GPUs, makes them the basic building block of data centers devoted to cloud applications or scientific computing. The way to optimal exploitation of such a wide amount of computing power passes through the ability of matching the best scheduling of hardware resources with the software characteristics of the applications. This requires appropriate models and evaluation methods. Simulation and analytical techniques are essential tools to support the design and the management process of such architectures, but a sound characterization of the workloads is required. Typical workloads consist in multithreaded applications, with different characteristics, that dynamically span over the cores of multiple machines, connected by fast networks. In this paper we propose several parametric performance models for different configurations of multicore machines, with or without GPU support, running multiple class multithreaded applications, aiming to supply a detailed modeling help for complex data centers. Copyright © 2015 John Wiley & Sons, Ltd.

Multicore architectures are now available for a wide range of high performance applications, ranging from embedded systems to large scale servers deployed in cloud environments. Multicore architectures are usually subject to two conflicting goals: obtaining a full utilization of the cores while achieving given performance objectives, such as throughput, response time or reduced energy consumption. Moreover, there is a strong interdependence between the software characteristics of the applications, and the underlying CPU architecture. In this scenario, simulation and analytical techniques can provide solid tools to properly design the considered class of systems: however, properly characterize the workload on multithreaded application in multicore environment is not an easy task, and thus is an hot research topic. In this paper we present several models, of increasing complexity, that can characterize multithreaded applications running on multicore architectures.

The paper discusses a Dynamic Markovian Agent Model obtained by adding mobility to a recently introduced new formalism suitable for the analysis of large scale systems, composed by a population of interacting entities, called Markovian Agents (MA). The differential equations describing the evolution of the MA density in time and space are derived, and their numerical solution is briefly sketched. An application to the analysis of the flow of vehicles in a road tunnel is discussed, together with the evaluation of the probability of collision against a fixed obstacle.

Power management in battery operated sensor networks, is a hot topic addressed by many ongoing researches. One of the most commonly employed technique consists in turning on and of the power of the radio unit to reduce the power consumption. In this paper we exploit the modelling power of Interacting Markovian agent to evaluate the performance of four different on-off strategies. A Markovian agent (MA) is an entity whose behaviour is described by a continuous time Markov chain (CTMC) and that is able to interact with other MA's by sending and receiving messages. A perceived message may induce a state transition in an MA according to a perception function than can depend on the geographical location of the MA's, on the message routing strategy and on the transmission property of the medium. We represent each sensor by an MA and we show how MA's can interact to produce the collective behaviour of the sensor network.