17,315 research outputs found
Antifragility = Elasticity + Resilience + Machine Learning: Models and Algorithms for Open System Fidelity
We introduce a model of the fidelity of open systems - fidelity being
interpreted here as the compliance between corresponding figures of interest in
two separate but communicating domains. A special case of fidelity is given by
real-timeliness and synchrony, in which the figure of interest is the physical
and the system's notion of time. Our model covers two orthogonal aspects of
fidelity, the first one focusing on a system's steady state and the second one
capturing that system's dynamic and behavioural characteristics. We discuss how
the two aspects correspond respectively to elasticity and resilience and we
highlight each aspect's qualities and limitations. Finally we sketch the
elements of a new model coupling both of the first model's aspects and
complementing them with machine learning. Finally, a conjecture is put forward
that the new model may represent a first step towards compositional criteria
for antifragile systems.Comment: Preliminary version submitted to the 1st International Workshop "From
Dependable to Resilient, from Resilient to Antifragile Ambients and Systems"
(ANTIFRAGILE 2014), https://sites.google.com/site/resilience2antifragile
Adaptive Process Management in Cyber-Physical Domains
The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the âphysicalâ real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time
Supporting adaptiveness of cyber-physical processes through action-based formalisms
Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system
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The THREAT-ARREST Cyber-Security Training Platform
Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organizationâs system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed
The Global Risks Report 2016, 11th Edition
Now in its 11th edition, The Global Risks Report 2016 draws attention to ways that global risks could evolve and interact in the next decade. The year 2016 marks a forceful departure from past findings, as the risks about which the Report has been warning over the past decade are starting to manifest themselves in new, sometimes unexpected ways and harm people, institutions and economies. Warming climate is likely to raise this year's temperature to 1° Celsius above the pre-industrial era, 60 million people, equivalent to the world's 24th largest country and largest number in recent history, are forcibly displaced, and crimes in cyberspace cost the global economy an estimated US$445 billion, higher than many economies' national incomes. In this context, the Reportcalls for action to build resilience â the "resilience imperative" â and identifies practical examples of how it could be done.The Report also steps back and explores how emerging global risks and major trends, such as climate change, the rise of cyber dependence and income and wealth disparity are impacting already-strained societies by highlighting three clusters of risks as Risks in Focus. As resilience building is helped by the ability to analyse global risks from the perspective of specific stakeholders, the Report also analyses the significance of global risks to the business community at a regional and country-level
A framework for Model-Driven Engineering of resilient software-controlled systems
AbstractEmergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones
Considerations about Continuous Experimentation for Resource-Constrained Platforms in Self-Driving Vehicles
Autonomous vehicles are slowly becoming reality thanks to the efforts of many
academic and industrial organizations. Due to the complexity of the software
powering these systems and the dynamicity of the development processes, an
architectural solution capable of supporting long-term evolution and
maintenance is required.
Continuous Experimentation (CE) is an already increasingly adopted practice
in software-intensive web-based software systems to steadily improve them over
time. CE allows organizations to steer the development efforts by basing
decisions on data collected about the system in its field of application.
Despite the advantages of Continuous Experimentation, this practice is only
rarely adopted in cyber-physical systems and in the automotive domain. Reasons
for this include the strict safety constraints and the computational
capabilities needed from the target systems.
In this work, a concept for using Continuous Experimentation for
resource-constrained platforms like a self-driving vehicle is outlined.Comment: Copyright 2017 Springer. Paper submitted and accepted at the 11th
European Conference on Software Architecture. 8 pages, 1 figure. Published in
Lecture Notes in Computer Science vol 10475 (Springer),
https://link.springer.com/chapter/10.1007/978-3-319-65831-5_
Governing in the Anthropocene: What Future Systems Thinking in Practice?
The revealing and concealing features of the metaphor âearth as Anthropoceneâ are explored in an inquiry that asks: In the Anthropocene what possible futures emerge for systems thinking in practice? Framing choice, so important yet so poorly realised, is the starting point of the inquiry. Three extant conceptual pathway-dependencies are unpacked: governance or governing; practice or practising and âsystemâ. New data on the organisational complexity within the field of cybersystemics is presented; new âimaginariesâ including systemic co-inquiry and institutional recovery are proposed as novel institutions and practices to facilitate systemic transformations within an Anthropocene setting. The arguments of the paper are illustrated through a research case study based on attempts to transform water and/or river situations towards systemic water governance. It is concluded that future systems research can be understood as the search for effective âimaginariesâ that offer fresh possibilities within an Anthropocene framing
Towards a Formal Model of Recursive Self-Reflection
Self-awareness holds the promise of better decision making based on a comprehensive assessment of a system\u27s own situation. Therefore it has been studied for more than ten years in a range of settings and applications. However, in the literature the term has been used in a variety of meanings and today there is no consensus on what features and properties it should include. In fact, researchers disagree on the relative benefits of a self-aware system compared to one that is very similar but lacks self-awareness.
We sketch a formal model, and thus a formal definition, of self-awareness. The model is based on dynamic dataflow semantics and includes self-assessment, a simulation and an abstraction as facilitating techniques, which are modeled by spawning new dataflow actors in the system. Most importantly, it has a method to focus on any of its parts to make it a subject of analysis by applying abstraction, self-assessment and simulation. In particular, it can apply this process to itself, which we call recursive self-reflection. There is no arbitrary limit to this self-scrutiny except resource constraints
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