Enabling Model-Driven Live Analytics For Cyber-Physical Systems: The Case of Smart Grids

Advances in software, embedded computing, sensors, and networking technologies will lead to a new generation of smart cyber-physical systems that will far exceed the capabilities of today’s embedded systems. They will be entrusted with increasingly complex tasks like controlling electric grids or autonomously driving cars. These systems have the potential to lay the foundations for tomorrow’s critical infrastructures, to form the basis of emerging and future smart services, and to improve the quality of our everyday lives in many areas. In order to solve their tasks, they have to continuously monitor and collect data from physical processes, analyse this data, and make decisions based on it. Making smart decisions requires a deep understanding of the environment, internal state, and the impacts of actions. Such deep understanding relies on efficient data models to organise the sensed data and on advanced analytics. Considering that cyber-physical systems are controlling physical processes, decisions need to be taken very fast. This makes it necessary to analyse data in live, as opposed to conventional batch analytics. However, the complex nature combined with the massive amount of data generated by such systems impose fundamental challenges. While data in the context of cyber-physical systems has some similar characteristics as big data, it holds a particular complexity. This complexity results from the complicated physical phenomena described by this data, which makes it difficult to extract a model able to explain such data and its various multi-layered relationships. Existing solutions fail to provide sustainable mechanisms to analyse such data in live. This dissertation presents a novel approach, named model-driven live analytics. The main contribution of this thesis is a multi-dimensional graph data model that brings raw data, domain knowledge, and machine learning together in a single model, which can drive live analytic processes. This model is continuously updated with the sensed data and can be leveraged by live analytic processes to support decision-making of cyber-physical systems. The presented approach has been developed in collaboration with an industrial partner and, in form of a prototype, applied to the domain of smart grids. The addressed challenges are derived from this collaboration as a response to shortcomings in the current state of the art. More specifically, this dissertation provides solutions for the following challenges: First, data handled by cyber-physical systems is usually dynamic—data in motion as opposed to traditional data at rest—and changes frequently and at different paces. Analysing such data is challenging since data models usually can only represent a snapshot of a system at one specific point in time. A common approach consists in a discretisation, which regularly samples and stores such snapshots at specific timestamps to keep track of the history. Continuously changing data is then represented as a finite sequence of such snapshots. Such data representations would be very inefficient to analyse, since it would require to mine the snapshots, extract a relevant dataset, and finally analyse it. For this problem, this thesis presents a temporal graph data model and storage system, which consider time as a first-class property. A time-relative navigation concept enables to analyse frequently changing data very efficiently. Secondly, making sustainable decisions requires to anticipate what impacts certain actions would have. Considering complex cyber-physical systems, it can come to situations where hundreds or thousands of such hypothetical actions must be explored before a solid decision can be made. Every action leads to an independent alternative from where a set of other actions can be applied and so forth. Finding the sequence of actions that leads to the desired alternative, requires to efficiently create, represent, and analyse many different alternatives. Given that every alternative has its own history, this creates a very high combinatorial complexity of alternatives and histories, which is hard to analyse. To tackle this problem, this dissertation introduces a multi-dimensional graph data model (as an extension of the temporal graph data model) that enables to efficiently represent, store, and analyse many different alternatives in live. Thirdly, complex cyber-physical systems are often distributed, but to fulfil their tasks these systems typically need to share context information between computational entities. This requires analytic algorithms to reason over distributed data, which is a complex task since it relies on the aggregation and processing of various distributed and constantly changing data. To address this challenge, this dissertation proposes an approach to transparently distribute the presented multi-dimensional graph data model in a peer-to-peer manner and defines a stream processing concept to efficiently handle frequent changes. Fourthly, to meet future needs, cyber-physical systems need to become increasingly intelligent. To make smart decisions, these systems have to continuously refine behavioural models that are known at design time, with what can only be learned from live data. Machine learning algorithms can help to solve this unknown behaviour by extracting commonalities over massive datasets. Nevertheless, searching a coarse-grained common behaviour model can be very inaccurate for cyber-physical systems, which are composed of completely different entities with very different behaviour. For these systems, fine-grained learning can be significantly more accurate. However, modelling, structuring, and synchronising many fine-grained learning units is challenging. To tackle this, this thesis presents an approach to define reusable, chainable, and independently computable fine-grained learning units, which can be modelled together with and on the same level as domain data. This allows to weave machine learning directly into the presented multi-dimensional graph data model. In summary, this thesis provides an efficient multi-dimensional graph data model to enable live analytics of complex, frequently changing, and distributed data of cyber-physical systems. This model can significantly improve data analytics for such systems and empower cyber-physical systems to make smart decisions in live. The presented solutions combine and extend methods from model-driven engineering, [email protected], data analytics, database systems, and machine learning

Towards Highly Scalable Runtime Models with History

Advanced systems such as IoT comprise many heterogeneous, interconnected, and autonomous entities operating in often highly dynamic environments. Due to their large scale and complexity, large volumes of monitoring data are generated and need to be stored, retrieved, and mined in a time- and resource-efficient manner. Architectural self-adaptation automates the control, orchestration, and operation of such systems. This can only be achieved via sophisticated decision-making schemes supported by monitoring data that fully captures the system behavior and its history. Employing model-driven engineering techniques we propose a highly scalable, history-aware approach to store and retrieve monitoring data in form of enriched runtime models. We take advantage of rule-based adaptation where change events in the system trigger adaptation rules. We first present a scheme to incrementally check model queries in the form of temporal logic formulas which represent the conditions of adaptation rules against a runtime model with history. Then we enhance the model to retain only information that is temporally relevant to the queries, therefore reducing the accumulation of information to a required minimum. Finally, we demonstrate the feasibility and scalability of our approach via experiments on a simulated smart healthcare system employing a real-world medical guideline.Comment: 8 pages, 4 figures, 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS2020

Distributed Graph Queries for Runtime Monitoring of Cyber-Physical Systems

In safety-critical cyber-physical systems (CPS), a service failure may result in severe financial loss or damage in human life. Smart CPSs have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous computation platform and provide autonomous behavior. This complexity makes design time verification infeasible in practice, and many CPSs need advanced runtime monitoring techniques to ensure safe operation. While graph queries are a powerful technique used in many industrial design tools of CPSs, in this paper, we propose to use them to specify safety properties for runtime monitors on a high-level of abstraction. Distributed runtime monitoring is carried out by evaluating graph queries over a distributed runtime model of the system which incorporates domain concepts and platform information. We provide a semantic treatment of distributed graph queries using 3-valued logic. Our approach is illustrated and an initial evaluation is carried out using an educational demonstrator of CPSs

Dagstuhl News January - December 2011

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Reflecting on the past and the present with temporal graph-based models

Self-adaptive systems (SAS) need to reflect on the current environment conditions, their past and current behaviour to support decision making. Decisions may have different effects depending on the context. On the one hand, some adaptations may have run into difficulties. On the other hand, users or operators may want to know why the system evolved in a certain direction. Users may just want to know why the system is showing a given behaviour or has made a decision as the behaviour may be surprising or not expected. We argue that answering emerging questions related to situations like these requires storing execution trace models in a way that allows for travelling back and forth in time, qualifying the decision making against available evidence. In this paper, we propose temporal graph databases as a useful representation for trace models to support self-explanation, interactive diagnosis or forensic analysis. We define a generic meta-model for structuring execution traces of SAS, and show how a sequence of traces can be turned into a temporal graph model. We present a first version of a query language for these temporal graphs through a case study, and outline the potential applications for forensic analysis (after the system has finished in a potentially abnormal way), self-explanation, and interactive diagnosis at runtime

A framework for engineering reusable self-adaptive systems

The increasing complexity and size of information systems result in an increasing effort for maintenance. Additionally, miniaturization of devices leads to mobility and the need for context-adaptation. Self-adaptive Systems (SASs) can adapt to changes in their environment or the system itself. So far, however, development of SASs is frequently tailored towards the requirements of use cases. The research for reusable elements — for implementation as well as design processes — is often neglected. Integrating reusable processes and implementation artifacts into a framework and offering a tool suite to developers would make development of SASs faster and less error-prone. This thesis presents the Framework for Engineering Self-adaptive Systems (FESAS). It offers a reusable implementation of a reference system, tools for implementation and design as well as a middleware for controlling system deployment. As a second contribution, this thesis introduces a new approach for self-improvement of SASs which complements the SAS with meta-adaptation

Requirements-aware models to support better informed decision-making for self-adaptation using partially observable Markov decision processes

A self-adaptive system (SAS) is a system that can adapt its behaviour in re- sponse to environmental fluctuations at runtime and its own changes. Therefore, the decision-making process of a SAS is challenged by the underlying uncertainty. In this dissertation, the author focuses on the kind of uncertainty associated with the satisficement levels of non-functional requirements (NFRs) given a set of design decisions reflected on a SAS configuration. Specifically, the focus of this work is on the specification and runtime handling of the uncertainty related to the levels of satisficement of the NFRs when new evidence is collected, and that may create the need of adaptation based on the reconfiguration of the system. Specifically, this dissertation presents two approaches that address decision-making in SASs in the face of uncertainty. First, we present RE-STORM, an approach to support decision- making under uncertainty, which uses the current satisficement level of the NFRs in a SAS and the required trade-offs, to therefore guide its self-adaptation. Second, we describe ARRoW, an approach for the automatic reassessment and update of initial preferences in a SAS based on the current satisficement levels of its NFRs. We eval- uate our proposals using a case study, a Remote Data Mirroring (RDM) network. Other cases have been used as well in different publications. The results show that under uncertain environments, which may have not been foreseen in advance, it is feasible that: (a) a SAS reassess the preferences assigned to certain configurations and, (b) reconfigure itself at runtime in response to adverse conditions, in order to keep satisficing its requirements

Innovationsforum open4INNOVATION2012 regional kooperativ-global innovativ: Beiträge zum Fachforum

Die Zukunft liegt bereits heute schon im Internet der Dinge, Daten, Dienste und Personen. Informations- und Kommunikationstechnologien (IKT) beeinflussen vermehrt die alltäglichen Abläufe, übernehmen im Ernstfall lebenserhaltende Körperfunktionen, unterstützen Arbeits- und Produktionsprozesse und halten Einzug in unsere Wohnbereiche. Dabei rückt der Gedanke einer anwendungsnahen und integrierten Sicht von Software zunehmend in den Vordergrund und verlangt deshalb interdisziplinäre Ansätze. Eine frühzeitige technische Abstimmung zwischen Soft- und Hardware sowie unterschiedlichen technischen Öko-Systemen wird dabei notwendiger und fordert Politik, Wissenschaft und Wirtschaft in gleichem Maße. Das Innovationsforum open4INNOVATION2012 am 9.Mai bot dazu Praktikern und Akademikern eine Plattform für den interdisziplinären und fachbereichsübergreifenden Austausch zu neuen und anwendungsnahen IKT-Ansätzen. Unter dem Motto regional kooperativ, global innovativ galt es dabei regional politische, wirtschaftliche und wissenschaftliche Kompetenzen zu bündeln, um globale Märkte erfolgreich zu bestreiten. In dem vorliegenden Tagungsband finden Sie die Beiträge des Fachforums, welches ein Hauptformat der Veranstaltung darstellte. Zusätzlich kam es auf dem Innovationsforum open4INNOVATION2012 erstmals zur aktiven Vernetzung sächsischer Forschergruppen, deren wissenschaftlicher Schwerpunkt die Robotik ist. Auf diesem ersten sächsischen Robotertreffen stand vor allem die Arbeit mit humanoiden Robotern im Mittelpunkt

Security Analysis of System Behaviour - From "Security by Design" to "Security at Runtime" -

The Internet today provides the environment for novel applications and processes which may evolve way beyond pre-planned scope and purpose. Security analysis is growing in complexity with the increase in functionality, connectivity, and dynamics of current electronic business processes. Technical processes within critical infrastructures also have to cope with these developments. To tackle the complexity of the security analysis, the application of models is becoming standard practice. However, model-based support for security analysis is not only needed in pre-operational phases but also during process execution, in order to provide situational security awareness at runtime. This cumulative thesis provides three major contributions to modelling methodology. Firstly, this thesis provides an approach for model-based analysis and verification of security and safety properties in order to support fault prevention and fault removal in system design or redesign. Furthermore, some construction principles for the design of well-behaved scalable systems are given. The second topic is the analysis of the exposition of vulnerabilities in the software components of networked systems to exploitation by internal or external threats. This kind of fault forecasting allows the security assessment of alternative system configurations and security policies. Validation and deployment of security policies that minimise the attack surface can now improve fault tolerance and mitigate the impact of successful attacks. Thirdly, the approach is extended to runtime applicability. An observing system monitors an event stream from the observed system with the aim to detect faults - deviations from the specified behaviour or security compliance violations - at runtime. Furthermore, knowledge about the expected behaviour given by an operational model is used to predict faults in the near future. Building on this, a holistic security management strategy is proposed. The architecture of the observing system is described and the applicability of model-based security analysis at runtime is demonstrated utilising processes from several industrial scenarios. The results of this cumulative thesis are provided by 19 selected peer-reviewed papers