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Organic Computing - A New Vision for Distributed Embedded Systems
Abstract
Organic computing is becoming the new vision for the design of complex systems, satisfying human needs for trustworthy systems that behave life-like by adapting autonomously to dynamic changes of the environment, and have self-x properties as postulated for autonomic computing. Organic computing is a response to the threatening view of being surrounded by interacting and self-organizing systems which may become unmanageable, showing undesired emergent behavior. Major challenges for organic system design arise from the conflicting requirements to have systems that are at the same time robust and adaptive, having sufficient degrees of freedom for showing self-x properties but being open for human intervention and operating with respect to appropriate rules and constraints to prevent the occurrence of undesired emergent behavior.
... Over the last two decades, systems researchers have acknowledged the increasing complexity in developing and managing software systems and the need for new and dynamic solutions [6,24,26,47]. According to Blair [6], this complexity stems from two main reasons: i) the increasing heterogeneity of modern systems, and ii) the increasing volatility ever more present in the operating environment of such systems. ...
... The illustrated situations demand fast and accurate decision making that may affect many different layers of the system. In the literature [26,40,47], the self-adaptive and autonomic computing communities have argued that pushing the responsibility of decision making to adapt systems when facing changes to the machine itself is the best way forward. ...
... The Organic Computing research initiative started in Germany with the focus to address and explore the self-organisation concept in technical software systems, inspired by neuroscience and molecular biology principles and software engineering [35,47,51]. Originated around the same time as the Autonomic Computing paradigm, the Organic Computing initiative also focused on the problem of having multiple instances of interacting autonomous systems, which may lead to conflicts and undesired emergent behaviour affecting the resulting system [47]. ...
Autonomic Computing and related research communities have drawn attention from re-searchers and industry practitioners who seek techniques and tools to build large-scale,reliable self-adaptive systems. However, building autonomic solutions remains a chal-lenge: i) the upfront effort to develop such systems is very high, making them costly toimplement; ii) only specialised parts of the system are made adaptive, limiting its flexi-bility in handling unknown operating conditions; and iii) state-of-the-art approaches stillheavily rely on design-time predictions of operating conditions, making systems execu-tion uncertain when predictions are wrong. To address these challenges, the concept ofEmergent Software Systems has been proposed. The Emergent Software approach aims toreduce the upfront effort to create autonomic solutions, and it supports fully adaptive sys-tems able to autonomously learn about the system’s structure and operating environmentwith no predefined knowledge or predictions. This chapter aims to disseminate EmergentSoftware Systems, presenting the central concept and tools to realise the approach.
... Systeme sollen robust, flexibel und widerstandsfähig gegenüber Störungen sein. Das Vertrauen in diese Systeme soll auch im Fehlerfall weiterhin bestehen können [4]. Ebenso ist eine möglichst geringe Kontroll-und Managementkomplexität das Ziel [3]. ...
... Ebenso ist eine möglichst geringe Kontroll-und Managementkomplexität das Ziel [3]. Des weiteren gilt es, mit der in Zukunft nicht mehr zu bewältigenden Entwurfskomplexität von uns umgebenden und miteinander interagierenden, intelligenten Systeme umzugehen [4]. Computersysteme sollen genug Freiheiten haben, damit diese sich an dieändernden Anforderungen ihrer Ausführungsumgebung anpassen können [4]. ...
... Des weiteren gilt es, mit der in Zukunft nicht mehr zu bewältigenden Entwurfskomplexität von uns umgebenden und miteinander interagierenden, intelligenten Systeme umzugehen [4]. Computersysteme sollen genug Freiheiten haben, damit diese sich an dieändernden Anforderungen ihrer Ausführungsumgebung anpassen können [4]. Dafür besitzen die Systeme Sensoren und Aktuatoren, welche das Aufnehmen der Bedingungen ihrer Umgebung, sowie das Interagieren mit dieser ermöglichen [3]. ...
Netzwerkprotokolle sind fundamental für den Aus-tausch von Daten im Internet. Trotz der dynamischen Natur des Internets sind diese Protokolle meist statisch oder eher begrenzt in ihrer Dynamik. Um dieses Problem anzugehen wurden verschiedene Ansätze entwickelt, dazu zählen das ONC System wie auch der Cactus Ansatz. Cactus beschreibt einen möglichen Aufbau für ein Netzwerkprotokoll, welcher eine hohe Konfigurierbarkeit und Anpassbarkeit bietet. Das Organic Network Control (ONC) System kann die Parameter eines Protokolls dynamisch und basierend auf den Umgebung-seinflüssen anpassen. Des weiteren ist es dazu in der Lage, die eigene Leistung selbstständig zu verbessern. Diese Arbeit vere-int beide Ansätze miteinander, um die hohe Konfigurierbarkeit des Cactus Ansatzes auszunutzen, damit das ONC System die Leistung des Protokolls möglichst weit steigern kann.
... For this, multiple papers, more specifically five for OC [10], [4], [6], [2], [3] and three for OTC [8], [5], [9], were evaluated. The papers on OC were analyzed to create classification criteria. ...
... Here three such qualities, chosen due to their appearance in multiple papers on OC, will be given together with their definitions. Firstly an OC system should be adaptive as mentioned in [10], [4], [6], [2]. An adaptive system has the ability to react to a set of disturbances in a way, that leads it back to an acceptable behavior [7]. ...
... Finally, an OC system should also display a certain degree of robustness according to [10], [4], [6], [2]. If a system can continue to show acceptable behavior with regard to a certain set of disturbances, such as local faults or attacks, it is considered robust. ...
... Like in other complex systems, the global behaviour of U-Healthcare systems might be unexpected. Owing to this complexity, innovative ideas for the design and the development of these systems must be found (Schmeck, 2005). Autonomic Computing and Organic Computing paradigms have been introduced in order to overcome the rising complexity of computing systems. ...
... The main objective of OC is to make systems more life-like by giving them the self-x proprieties of autonomic computing (Kephart and Chess, 2003); such as self-organisation, self-configuration, self-healing, self-optimisation and selfprotection and can also expand towards self-adaptivity and self-awareness. One of the key statements of the OC is the following: "It is not the question whether self-organised and adaptive systems will arise but how they will be designed" (Schmeck, 2005). The most significant aspect of OC systems is the autonomic adaptability to the dynamic environmental changes in response to the human needs; by giving the system ability of sensing their current situation and providing appropriate responses to dynamically changing environmental conditions (by giving them adequate degrees of freedom). ...
... This complexity calls for new innovative design ideas of these systems. As stated in Schmeck (2005); "It is not the question whether selforganised and adaptive systems will arise but how they will be designed". The combination of O/C architecture with ontologies and rule-based-approaches provides new design approach for designing and developing pervasive healthcare systems. ...
This paper proposes a new design approach for U-Healthcare systems. Our approach consists in combining the generic observer/controller architecture of organic computing, ontologies and rule-based paradigms. This combination brings three significant benefits. First, it allows keeping the system highly supervised and controlled by the observer/controller. Secondly, it enables the system to reason upon useful contextual information gathered from different and heterogeneous entities, and deduce new situations that require adaptation of the system's behaviour. Finally, it enables the system to adapt its behaviour dynamically to thecontext change by reasoning about this context and selecting the appropriate service to a specific user.
... Specifically local behavior of individual nodes can lead to novel global behavior that may look entirely different and cannot be directly predicted from the locally applied rules. However, this means that these kinds of systems may behave in unexpected ways [5]. ...
... Additionally, an Organic System is to be resilient to the failure of connections or entire nodes [5]. ...
Increasing growth in the field of IoT networks where bandwidth is limited and both individual nodes as well as the entire network are vulnerable to external influences calls for dynamic control systems that can change network parameters on the fly. This paper investigates whether a centralized system with global knowledge or an organic system with locally self-optimizing nodes is the more viable solution. The Recursive Random Search Algorithm and Organic Network Control are selected as exemplary realizations of the respective approach.
... Organic Computing (called OC in the reminder of this chapter) is a German research initiative whose object of study are technical complex systems (ocwebsite, 2012;Schmeck, 2005;Würtz, 2008). OC intends to provide a full framework to study and design technical complex systems. ...
... L'approche dite « Organic Computing » (appellée OC dans le reste du chapitre) est une initiative de recherche allemande portant sur l'étude de systèmes complexes technologiques (ocwebsite, 2012;Schmeck, 2005;Würtz, 2008). Cette approche propose un cadre d'étude complet pour étudier et créer des systèmes complexes technologiques. ...
Complex systems are present everywhere in our environment: internet, electricity distribution networks, transport networks. This systems have as characteristics: a large number of autonomous entities, dynamic structures, different time and space scales and emergent phenomena. This thesis work is centered on the problem of control of such systems. The problem is defined as the need to determine, based on a partial perception of the system state, which actions to execute in order to avoid or favor certain global states of the system. This problem comprises several difficult questions: how to evaluate the impact at the global level of actions applied at a global level, how to model the dynamics of an heterogeneous system (different behaviors issue of different levels of interactions), how to evaluate the quality of the estimations issue of the modeling of the system dynamics. We propose a control architecture based on an ``equation-free'' approach. We use a multi-agent model to evaluate the global impact of local control actions before applying the most pertinent set of actions. Associated to our architecture, an experimental platform has been developed to confront the basic ideas or the architecture within the context of simulated ``free-riding'' phenomenon in peer to peer file exchange networks. We have demonstrated that our approach allows to drive the system to a state where most peers share files, despite given initial conditions that are supposed to drive the system to a state where no peer shares. We have also executed experiments with different configurations of the architecture to identify the different means to improve the performance of the architecture
... The OC Initiative was launched in Germany in 2003 to master complex systems [19]. Biological processes were identified as a major model for the increasing complexity of embedded systems and their mechanisms of self-organisation were transferred to current technologies and problems [15]. The self-X properties are derived and supplemented for the OC at practically the same time, which are referred to as self-* properties in current development [18]. ...
... In 2003, the German Organic Computing Initiative was founded. In brief, it aims to improve the control ability of complex embedded systems based on principles found in organic systems [16]. Another research paper [3] motivates the combination of organic computing based on artificial DNA with adaptive online diagnosis techniques for safety-critical application domains. ...
In the future, autonomous systems such as self-driving cars must robustly and flexibly handle various fault situations. Static models of faults and countermeasures are standard in classical approaches; however, such static models are no longer efficient as the complexity of fault scenarios tremendously increased. The bio-inspired concept of organic computing applies biological concepts to technical systems. Organic computing utilizes an artificial hormone system as a decentralized mechanism, i.e., a middleware that continuously monitors and organizes task allocations to computing nodes in distributed real-time embedded systems. By introducing different types of artificial hormones for the tasks, task allocations are realized by constantly establishing hormone balances via distributed closed control loops. This process handles the increasing complexity of e.g., distributed control systems by enabling self-configuration, -adaptation, -improvement and -healing. Such an organic computing environment inherently overcomes system-level faults, such as computation node failures, since missing (i.e., non-executed) tasks directly lead to hormone imbalances that are compensated for, thereby restoring these tasks. However, faults in the artificial hormone system, e.g., due to incorrect hormone values, are currently not covered by the organic computing environment and can result in adverse and critical system behavior and even complete system failure. To address these types of faults and thus improve the capabilities and safety of current organic computing systems, this paper presents a fault injection framework to analyze the effects of an extended range of fault cases, including faults in the artificial hormone system. Such analyses are important as they mark the foundation for future fault-handling strategies and safety features in next-generation organic computing systems for autonomous systems. The statistical analyses and results based on the fault injection framework reveal the most safety-related fault cases for which the paper outlines respective fault handling strategies.
... New software systems such as the Drona framework for mobile robots [74] focus on making algorithms and control processes autonomous, distributed and decentralized in a safe manner, such that time desynchronization and communications faults will not impede operation. Other visions of how distributed self-managing systems have been similarly formulated to address complexity and reliability such as that of Organic Computing, which focuses on the effects of emergent behaviours in massively connected self-organizing systems [75]. Another concept of computing related by the goals of system fractionability and complexity minimalization is that of Wide Computing, in which concurrent tasks are spread horizontally across a network of visible, connected elements in a peer-to-peer fashion [60]. ...
This paper presents a review of modular and reconfigurable space robot systems intended for use in orbital and planetary applications. Modular autonomous robotic systems promise to be efficient, versatile, and resilient compared with conventional and monolithic robots, and have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. Based on a set of fundamental concepts in modular self-reconfiguring robotics, advances in applying modular self-organizing robotics technologies to aerospace applications and space mission concepts are summarized for the purpose of identifying relevant requirements and solutions. Based on this survey, critical guidelines for the implementation of in space assembly and operation using modular autonomous robotic systems are identified.
... Deployment independence is then achieved by a suitable mapping of a general self-organization problem to the actual platform, and performed by spreading such chunks in the available devices. Large-scale distributed and self-adaptive systems as envisioned in forthcoming CPS and the Internet-of-Things (IoT) make a case for distributed declarative programming promoting self-organization, while at the same time leaving degrees of freedom [59] to designers and deployment. We highlight several representative research efforts on deployment and automatic reconfiguration of systems. ...
Emerging cyber-physical systems, such as robot swarms, crowds of augmented people, and smart cities, require well-crafted self-organizing behavior to properly deal with dynamic environments and pervasive disturbances. However, the infrastructures providing networking and computing services to support these systems are becoming increasingly complex, layered and heterogeneous—consider the case of the edge–fog–cloud interplay. This typically hinders the application of self-organizing mechanisms and patterns, which are often designed to work on flat networks. To promote reuse of behavior and flexibility in infrastructure exploitation, we argue that self-organizing logic should be largely independent of the specific application deployment. We show that this separation of concerns can be achieved through a proposed “pulverization approach”: the global system behavior of application services gets broken into smaller computational pieces that are continuously executed across the available hosts. This model can then be instantiated in the aggregate computing framework, whereby self-organizing behavior is specified compositionally. We showcase how the proposed approach enables expressing the application logic of a self-organizing cyber-physical system in a deployment-independent fashion, and simulate its deployment on multiple heterogeneous infrastructures that include cloud, edge, and LoRaWAN network elements.
... Here, SuOC designates system under observation and control. It is composed of a set of interacting elements/ agents, and it does not depend on the existence of observer/controller [36,37]. ...
... It can be seen as an extension of the Autonomic computing vision of IBM (Figure 8). OC is based on the insight that we will soon be surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicate freely and organise themselves in order to perform the actions and services that seem to be required (Schmeck, 2005). OC is based on the observer/controller architecture. ...
Adaptive embedded systems (AES) are embedded systems whose behaviour and architecture are adjusted in run time to fulfil changing requirements. Despite the huge effort furnished by researchers to develop design methodologies and their associated tools for AES, we can easily observe that none of the existing approaches provides a complete development process covering all development phases and supporting traceability between their produced artefacts. Furthermore, most existing tools are not interoperable. Thus, a standard or at least a domain-specific methodology will be needed for an effective cost/delay design of AES. In this paper, we review AES modelling techniques and design methodologies. At the end of the paper, we will illustrate the main challenges confronted by AES design and some future perspectives.
... Here SuOC designates System under observation and control. It is composed of a set of interacting elements/agents and it does not depend on the existence of observer/controller. Figure 7 shows the distributed version of the OC architecture; here, each system component includes an observer/controller (Schmeck, 2005(Schmeck, , 2009). Recently, a new class of self adaptive SOCs emerges as a new paradigm inspired from the organic computing and especially the self-x properties. ...
Intelligent embedded systems (IES) and their distributed versions, represent a novel and promising generation of embedded systems. IES have the capacity of reasoning about their external environments and adapt their behaviour accordingly. Such systems are situated in the intersection of two different branches, which are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimising and self-adaptation. Despite, the wide spread of such systems, some design challenging issues are arising. Designing a resource constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded systems researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality.
... Dabei soll einerseits Systemen mehr Freiheiten eingeräumt werden, sich selbst zu organisieren, um auf unvorhergesehene Änderungen in ihrer Umgebung reagieren zu können. Andererseits muss das System robust gegenüber unerwünschtem Verhalten sein, mit der expliziten Möglichkeit des menschlichen Eingreifens [3], [4]. OCS sind hierbei als Konzept zur Bewältigung von Komplexität und nicht als feste Definition zu verstehen. ...
Organische Computersysteme sollen durch flexibles und automatisches Handeln auf komplexe Situationen reagieren können. Sie zeichnen sich durch sogenannte Selbst-Eigenschaften aus und sollen Ansprüchen stärker vernetzter, komplexer und autonomer Computersysteme gerecht werden. Ein holonisches Energiesystem soll neue Anforderungen eines unvorhersehbaren, dezentralisierten Energienetzwerkes beherrschen. Es werden zu diesem System die holonische Architektur, Integrationsmuster und Anwendungsszenarien vorgestellt. Da das holonische Energiesystem konzeptuell Selbst-Eigenschaften erfüllt, qualifiziert es sich als Organisches Computersystem.
... This technology targets machines interacting in organic behaviour e.g. self-organisation, self-configuration, self-healing, self-optimization or self-protection (Schmeck, 2005). Transferred to mechanical engineering, intelligent modules of a cyber-physical system could have the following characteristics: ...
A high level of industrial automation of repetitive tasks allows companies to efficiently produce products at large scale. Digitalization is the subsequent step of industrial automation and aims to further reduce costs and waiting times. Digitalization also aims to automate individual decision making. Key to both goals is to transform business processes from the analog to the digital world and then to analyze and thus to take advantage of digitized information. In this chapter, we provide an intuitive introduction to digitalization in mechanical engineering. We then present various business opportunities and discuss the related challenges. Next, we propose how mechanical engineering companies need to align their mindset with the digital transformation. Last, we present some of our works on digitalization in mechanical engineering and share a number of best practices. As an outcome, you will be able to employ digitalization in order to create real value in your business. That increase of efficiency will allow you to remain competitive in an environment that keeps becoming more and more competitive.
... It can be seen as an extension of the Autonomic computing vision of IBM (Figure 8). OC is based on the insight that we will soon be surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicate freely and organise themselves in order to perform the actions and services that seem to be required (Schmeck, 2005). OC is based on the observer/controller architecture. ...
... It can be seen as an extension of the Autonomic computing vision of IBM ( Figure 8). OC is based on the insight that we will soon be surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicate freely and organise themselves in order to perform the actions and services that seem to be required (Schmeck, 2005). OC is based on the observer/controller architecture. ...
... This also holds true for other classical middleware approaches. Self-organizing middleware architectures have been researched in the frame of the Organic Computing initiative [5]. There, bioinspired principles are used to acquire so-called self-x capabilities. ...
Cyber-physical systems (CPSs) are omnipresent these days and are used in various application fields. In those, several CPS have to cooperate to achieve their goals. Thus, cyber-physical networking plays an important role. The applications of CPSs may use different programming abstractions for the interaction. Also CPSs are used in changing environments and are consequently afflicted with dynamic non-functional requirements. Therefore, an adaptive communication with an end-to-end view of the system is important. The goal of our research is a middleware designed to the needs of cyber-physical networking which eases the development, test and maintenance of CPSs.
... DCOM [9], .NET [10] and Java RMI [8]. Self-organizing middleware architectures have been researched in the frame of the Organic Computing initiative [12]. There, bio-inspired principles are used to acquire socalled self-x capabilities. ...
Cyber-physical systems (CPSs) are used in various application fields. In those, multiple CPSs can cooperate by exchanging information (processed sensor data, derived data, etc.) to fullfill a task. Thus, cyber-physical networking plays an essential role. The communication in a cyber-physical network (CPN) as well as with the local sensors and actuators is complex. The applications of CPSs may use different programming abstractions, e.g. callbacks, streams, etc. for the interaction. Also CPNs are used in changing environments. Thus, they are afflicted with dynamic non-functional requirements. Due to this, an adaptive communication is important to enforce the resulting Quality of Service (QoS) constrains. Classical middleware architectures do not cover all of these aspects. In this paper we propose an architectur for a middleware that is designed to the needs of cyber-physical networking. Therefore, we strive to integrate multiple programming abstractions and communication protocols in an easy to use middleware. Also, we focus on the QoS-aspects of the communication to sensors, actuators and other CPS. Those constrains require an end-to-end view of system and a communication architecture that adapts to the changing available communication infrastructure in CPNs and correspondingly in CPSs. Therefore, this approach eases the development, test and maintenance of CPSs and CPNs.
... 5 Other visions of how self-managing systems have been similarly formulated to address complexity and reliability such as that of Organic Computing, which focuses on the effects of emergent behaviours in massively connected self-organizing systems. 6 Another concept of computing related by the goals of system fractionability and complexity minimization is that of Wide Computing, in which concurrent tasks are spread horizontally across a network of visible, connected elements in a peer-to-peer fashion. 7 In the broad context of autonomic systems, four additional characteristics are often cited: 8 ...
In this research, we aim to develop a knowledge-based self-reconfiguring laboratory demonstrator made up of cubic modules for validating self-reconfiguration strategies for modular satellites. Modular satellite technologies have the potential to reduce the costs and complexity of on-orbit assembly and servicing operations, as well as reducing waste for end-of-life satellites and required mass to orbit. Modules are sized to be 10cm cubes comparable to the 1U CubeSat form factor, and can connect and communicate at will to form larger structures. Each module can carry different payloads and perform different functions, while retaining common abilities to execute software and store information. Laboratory testing of self-reconfiguration strategies for a modular structure focuses on a key challenge essential to reconfigurable satellites: the ability to autonomously determine and achieve appropriate configurations of structures in orbit without human input. We apply knowledge-based inference methods to determining a suitable configuration of modules for a modular satellite and reasoning methods for planning the sequence of actions required to implement this configuration. In addition to satellites and space structures, self-assembling hardware represents a platform that could potentially be used to build many kinds of terrestrial robots and autonomous systems.
... Organic Computing (OC) (see also Autonomic Computing (AC) [34]) addresses complex technical systems that will self-adapt to new environmental conditions at runtime [47]. Key technologies are self- * techniques inspired by nature (e.g., self-configuration, self-organization, or self-optimization) [56]. Open challenges in the field that will be in the focus of the CIL initiative are: ...
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.
... It can be seen as an extension of the Autonomic computing vision of IBM. OC is based on the insight that we will soon be surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicate freely, and organize themselves in order to perform the actions and services that seem to be required (Schmeck, 2005). OC is based on the observer/controller architecture. ...
Adaptive Embedded Systems (AES) are a special class of intelligent embedded systems whose behavior and architecture are adjusted in run time to fulfill changing requirements. Despite the huge effort furnished by researchers to develop design methodologies and their associated tools for AES, we can easily observe that none of the existing approaches provides a complete development process covering all development phases and supporting traceability between their produced artifacts. Furthermore, most existing tools are not interoperable. Thus, a standard or at least a domain-specific methodology will be needed for an effective cost/delay design of AES. In this paper, we try to study and discuss the well known formalisms used for AES modeling and design methodologies. At the end of the paper, we will illustrate the main challenges confronted by AES design and some new tendencies.
... Life [118], Biomimetic [119], Organic Computing [120], and Genetic Algorithms [121]. In addition, theories such as the Self and Non-self, and Danger Theory [62], have been coined out with their primary premise on inspirations from biology. ...
... Here SuOC designates System under observation and control. It is composed of a set of interacting elements/agents and it does not depend on the existence of observer/controller. Figure 7 shows the distributed version of the OC architecture; here, each system component includes an observer/controller (Schmeck, 2005(Schmeck, , 2009). Recently, a new class of self adaptive SOCs emerges as a new paradigm inspired from the organic computing and especially the self-x properties. ...
Intelligent embedded systems (IES) and their distributed versions, represent a novel and promising generation of embedded systems. IES have the capacity of reasoning about their external environments and adapt their behaviour accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimising and self-adaptation. Despite, the wide spread of such systems, some design challenging issues are arising. Designing a resource constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded systems researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality.
... Based on the design in the previous section, a prototype of the generic user interface has been implemented and applied to a building operating system, the Organic Smart Home (OSH) (Allerding and Schmeck 2011), which is based on the Observer/Controller architecture in Organic Computing (Schmeck 2005). With the aid of evolutionary algorithms, the OSH optimizes the schedule of appliances so as to minimize energy costs for residents. ...
Abstract Building operating systems play an important role in monitoring energy consumption of devices and improving energy efficiency in household buildings. From this arises a need for a preferably flexible and full-featured user interface to visualize the energy data in the building and allow residents to collect and realize various needs and preferences to the system. This article introduces a generic user interface for building operating systems which is presented from aspects of design, implementation and evaluation. To ensure the user interface can be flexibly adapted to various types of buildings, we design a series of generic data models which are independent of any building operating system. Besides, three roles with different permissions and a number of functional components of the user interface are also introduced in the article. Based on the design, a prototype of such a generic user interface named Building Operating System User Interface (BOS UI) has been implemented to operate the Energy Smart Home Lab (ESHL) at the Karlsruhe Institute of Technology (KIT). We evaluate the design, functionality and usability of the BOS UI qualitatively and quantitatively. The evaluation results show that the BOS UI meets a set of desired requirements (except for system configuration) for a generic user interface of building operating systems. Besides this, the evaluation experiments yielded very positive feedback in many aspects including improvement of energy efficiency and user experience. More than 90% of the test users agreed that the BOS UI provided them with enough information and functionalities that they would need in their daily lives and it can help them to save money. Furthermore, the mean score of the System Usability Scale (SUS) is 79.0, which indicates a good usability. The experiments prove that the user interface is still easy to use, despite abundant features are integrated into the system.
... Here, SuOC designates system under observation and control. It is composed of a set of interacting elements/ agents, and it does not depend on the existence of observer/controller [36,37]. ...
... Schmeck 2005;Herrmann et al. 2005;Branke et al. 2006;Serugendo et al. 2011;Papazoglou 2012;Marinescu 2017). ...
New use scenarios, workloads, and increased heterogeneity combined with rapid growth in adoption are increasing the management complexity of cloud computing at all levels. High performance computing (HPC) is a particular segment of the IT market that provides significant technical challenges for cloud service providers and exemplifies many of the challenges facing cloud service providers as they conceptualise the next generation of cloud architectures. This chapter introduces cloud computing, HPC, and the challenges of supporting HPC in the cloud. It discusses how heterogeneous computing and the concepts of self-organisation, self-management, and separation of concerns can be used to inform novel cloud architecture designs and support HPC in the cloud at hyperscale. Three illustrative application scenarios for HPC in the cloud—(i) oil and gas exploration, (ii) ray tracing, and (iii) genomics—are discussed.
In dieser Dissertation wird die Entwicklung eines selbstorganisierenden Produktionssystems untersucht, um die Herausforderungen in der Produktionsplanung und -steuerung, insbesondere bei Unsicherheiten, besser zu bewältigen. Die Arbeit analysiert zunächst die Grundlagen der Produktionsplanung und -steuerung, gefolgt von einer detaillierten Untersuchung des interdisziplinären Themas der Selbstorganisation. Basierend darauf wird ein Vorgehen zur Identifikation geeigneter Methoden der Selbstorganisation für eine bestimmte Problemstellung entwickelt und auf Produktionsplanung und -steuerung angewendet. Durch die Bewertung bestehender Referenzarchitekturen und deren Integration in ein Framework für selbstorganisierende Produktion, werden selbstorganisierende Verfahren mit etablierten Verfahren der Produktionsplanung und -steuerung verglichen. Die Arbeit zeigt die Überlegenheit der selbstorganisierenden Systeme bei der Planung unter Unsicherheit und liefert nachweislich Verbesserungen hinsichtlich der sekundären und tertiären Ziele der Produktion. Die Dissertation gliedert sich in acht Kapitel, die jeweils verschiedene Aspekte der Selbstorganisation, Produktionsplanung und -steuerung, sowie die Entwicklung und Evaluation eines selbstorganisierenden Produktionssystems behandeln. Die Ergebnisse dieser Arbeit haben das Potenzial, die Produktionsplanung und -steuerung unter Unsicherheit effektiver zu gestalten und somit eine bedeutende Verbesserung für die Industrie zu bieten.
Cyber Physical Systems (CPS) are growing more and more complex due to the availability of cheap hardware, sensors, actuators and communication links. A network of cooperating CPSs (CPN) additionally increases the complexity. Furthermore, CPNs are often deployed in dynamic, unpredictable environments and safety-critical domains, such as transportation, energy, and healthcare. In such domains, usually applications of different criticality level exist. As a result of mixed-criticality, applications requiring hard real-time guarantees compete with those requiring soft real-time guarantees and best-effort application for the given resources within the overall system.This poses challenges as well as it offers chances: the increasing complexity makes it harder to design, operate, optimize and maintain such CPNs. However, on the other side an appropriate use of the increasing resources in computational nodes, sensors, actuators can significantly improve the system performance, reliability and flexibility. Hence, Organic Computing concepts like self-X features (self-organization, self-adaptation, self-healing, etc.) are key principles for such systems.Therefore, the comprehensive adaptive middleware Chameleon has been developed which applies such principles for CPNs. In this paper, the self-adaptation mechanism of Chameleon based on a MAPE-K loop and learning classifier systems is examined and evaluated. The results show its effectivity in autonomously handling the system resources to keep the required constraints of the applications with respect to their criticality.Keywordsadaptive middlewaremixed-criticalitycyber-physical systemscyber-physical networksMAPE-Klearning classifier systems
The Artificial DNA (ADNA) is a powerful tool for designing self-organizing, self-healing and self-configuring distributed embedded systems. However, a large amount of knowledge on the targeted hardware, available sensors, is required, thus limiting the reusability and adaptability of an already composed ADNA. Recently, the abstract ADNA () has been proposed as a countermeasure to this problem. In an , sensor elements are replaced by so-called abstract sensors describing properties of the required sensory input. Only when the is initialized on the target hardware, these abstract sensors are specified by a combination of actual sensors available. In addition, a semantic knowledge base provides knowledge on the hardware’s sensors and their relations. In order to convert an to a hardware specific ADNA, knowledge about how to calculate a required sensor value that cannot be directly measured by the hardware from other available sensors is required. In this paper, we present and analyze two algorithms that determine this knowledge.KeywordsArtificial DNASemanticsOrganic ComputingVirtual SensorsEmbedded Systems
One approach to handle the ever-increasing complexity of embedded systems is the Artificial Hormone System (AHS). The AHS is a middleware based on Organic Computing principles capable of assigning tasks to a distributed system’s processing elements (PEs). It is completely decentralized and has no single point of failure. In case a PE fails, the affected tasks are automatically re-assigned to healthy PEs, thus self-healing the system. Furthermore, the AHS is suited for real-time systems since hard time bounds can be proven for the duration of its self-configuration and self-healing capabilities. A recently proposed extension of the AHS supports defining assignment priorities for tasks. These allow to enforce a specific order of task assignment, thus allowing to e.g. start the most important tasks first during the system’s initial self-configuration and to make sure these tasks are re-assigned as quickly as possible in self-healing scenarios. Although this priority-based AHS extension’s time bounds have previously been studied, its behavior has not yet been thoroughly evaluated. In this paper, we thus present evaluations of this extension, confirming and refining the known time bounds.
Macroprogramming refers to the theory and practice of expressing the macro(scopic) behaviour of a collective system using a single program. Macroprogramming approaches are motivated by the need of effectively capturing global/system-level aspects and the collective behaviour of multiple computational components, while abstracting over low-level details. Previously, this programming style had been primarily adopted to describe the data-processing logic in sensor networks ; recently, research forums on spatial computing , collective systems , and the Internet of Things have provided renewed interest in macro-approaches. However, related contributions are still fragmented and lack conceptual consistency. Therefore, to foster principled research, an integrated view of the field is provided, together with opportunities and challenges.
Large pervasive systems, deployed in dynamic environments, require flexible control mechanisms to meet the demands of chaotic state changes while accomplishing system goals. As centralized control approaches may falter in environments where centralized communication and knowledge may be impossible to implement, researchers have proposed decentralized control methods that leverage agent-driven, self-organizing behaviors, to achieve reliable, flexible systems. This article presents and compares the performance of three decentralized control approaches in the online multi-object k -assignment problem. In this domain, a set of sensors is tasked to detect and track an unknown and changing set of targets. Results show that a proposed hybrid approach that incorporates supervisory devices within the population while allowing semi-autonomous operations in non-supervisory devices produces a flexible and reliable system capable of both high detection and coverage rates.
Augmented Reality (AR) is one of the most modern and attractive information visualization technologies. Despite the proliferation of AR with the spread of mobile devices, the technology is still limited in providing relevant and personalized experiences. This limitation inspired the idea to incorporate human needs in an augmented reality system to enable personalized and focused suggestions which is a step closer to achieving pervasiveness. The main question is: How to detect human needs from sensed data and provide augmented reality experiences to satisfy the needs? The research gives an overview of the analysis of data elements and sensor requirements for the fundamental human needs defined by Manfred Max-Neef; a proof-of-concept prototype that enables need detection; prediction of the subsistence, protection, and leisure needs from analyzed data; and recommendations on augmented reality experiences based on human needs. An experiment is conducted, data analysis and predictive modelling techniques are applied to the Context-Aware Personalization for Augmented Reality (CAP-AR) dataset to achieve the research objectives. A reflection on the data requirements to predict human needs and implications on planning and design of pervasive applications to detect and satisfy human needs constitutes the research results.KeywordsDataAnalyticsHuman needsNeeds detectionAugmented reality
In dieser Arbeit wird aufgezeigt, wie Organic Computing in der Robotik angewendet
werden kann, um Roboter zu designen, die autonom in unstrukturierten und unvorhersehbaren Umgebungen arbeiten können. Organic Computing ist ein Konzept, Eigenschaften von lebenden Organismen auf technische Systeme zu übertragen. Hierunter fallen meist sogenannte Selbst-Eigenschaften, wie z.B. Selbst-Organisation, Selbst-Optimierung und Selbst-Heilung. Zunächst soll der Begriff des Organic Computing definiert werden. Anschließend wird die "Organic Robot Control Architecture" (ORCA) vorgestellt, welche den Anspruch hat, eine Architektur für Roboter zu sein, die diesen mit Eigenschaften von Organic Computing austattet. Schließlich wird untersucht, inwiefern Prinzipien aus Organic Computing innherhalb von ORCA Anwendung finden.
This paper presents a priority-based task distribution strategy as an extension to the Artificial Hormone System (AHS). The AHS is a distributed middleware based on self-organization principles. It allows to distribute tasks to processing nodes in a self-organizing way while neither having a single-point-of-failure nor requiring external user input. Node failures are detected automatically, resulting in relocation of any affected tasks to operational nodes. This provides self-healing capabilities if sufficient computational resources are available.
Embedded systems are growing very complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields, e.g. in autonomous cars. Bio-inspired techniques like self-organization are a key feature to handle the increasing complexity of embedded systems. In biology the structure and organization of a system is coded in its DNA, while dynamic control flows are regulated by the hormone system. We adapted these concepts to embedded systems using an artificial DNA (ADNA) and an artificial hormone system (AHS). Based on these concepts, highly reliable, robust and flexible systems can be created. These properties predestine the ADNA and AHS for the use in future automotive applications.
However, computational resources and communication bandwidth are often limited in automotive environments. Furthermore, in many critical areas, the AUTOSAR Classic Platform in combination with CAN bus is used as a static operating system. Nevertheless, in this paper we show that the dynamic concept of ADNA and AHS can successfully be applied to a static system like the AUTOSAR Classic Platform and that the available computational resources are more than sufficient for automotive applications. The major bottleneck becomes the CAN bus communication when implemented on top of AUTOSAR’s communication stack as this limits the maximum achievable throughput for a single device in order to provide bandwidth for numerous different participants. Implementing the CAN bus communication directly through AUTOSAR’s CAN driver mostly removed this problem.
Furthermore, we lay the foundations of our concept’s adaption to the AUTOSAR Adaptive Platform, thus enabling interesting interoperation scenarios for ADNA-based automotive systems implemented on both AUTOSAR platforms.
Embedded systems are growing very complex because of the increasing chip integration density, larger number of chips in distributed applications, and demanding application fields, eg, in autonomous cars. Bio‐inspired techniques like self‐organization are a key feature to handle this complexity. In biology, the structure and organization of a system is coded in its DNA. We adapted this concept to embedded systems using an artificial DNA (ADNA). Based on the ADNA, the self‐organization mechanisms can build the system autonomously at run‐time providing a self‐building system. This property predestines the ADNA for the use in automotive applications because modern (autonomous) cars include several highly redundant processors (electronic control units (ECUs)). The ADNA can be used to reduce the number of ECUs in a car on the one hand and to make better use of the cars' redundant ECUs on the other hand. Our contribution in this paper is to evaluate the improvements possible due to the ADNA by analyzing the fail‐operational limits and failure probabilities in such scenarios. We also propose a simple graceful degradation scheme for the tasks to improve the system dependability of the cars. Finally, the usability of the concept is demonstrated by a practical evaluation.
Embedded systems are growing very complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields e.g. in autonomous cars. Bio-inspired techniques like self-organization are a key feature to handle the increasing complexity of embedded systems. In biology the structure and organization of a system is coded in its DNA, while dynamic control flows are regulated by the hormone system. We adapted these concepts to embedded systems using an artificial DNA (ADNA) and an artificial hormone system (AHS). Based on these concepts, highly reliable, robust and flexible systems can be created. These properties predestine the ADNA and AHS for the use in future automotive applications.
The early stages of emergence of technologies provide unique opportunities to observe and analyze the process of their assimilation into mainstream IT systems. The 3D virtual world (VWs) technology is one such technology. It is emerging as the next platform for developing systems with immersive and dynamic environments. This article investigates the unique aspects of building systems in VWs, and develops the Theory of Organic Emergence of Systems (TOES). Furthermore, the synthesis of TOES with the existing approaches of systems development suggests an integrative framework for systems development. The method of analysis is multiple case studies using Grounded Theory. The article makes multiple contributions. It has significant theoretical and empirical implications in understanding the process of building systems with emerging technologies. Furthermore, the integrative lens provides a unifying framework for different and often divergent views regarding the choice of suitable methods for systems development.
The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible.
Nowadays, large distributed databases are commonplace. Client applications increasingly rely on accessing objects from multiple remote hosts. The Internet itself is a huge network of computers, sending documents point-to-point by routing packetized data over multiple intermediate relays. As hubs in the network become overutilized, slowdowns and timeouts can disrupt the process. It is thus worth to think about ways to minimize these effects. Caching, i.e. storing replicas of previously-seen objects for later reuse, has the potential for generating large bandwidth savings and in turn a significant decrease in response time.
En-route caching is the concept that all nodes in a network are equipped with a cache, and may opt to keep copies of some documents for future reuse [18]. The rules used for such decisions are called “caching strategies”. Designing such strategies is a challenging task, because the different nodes interact, resulting in a complex, dynamic system. In this paper, we use genetic programming to evolve good caching strategies, both for specific networks and network classes. An important result is a new innovative caching strategy that outperforms current state-of-the-art methods.
This paper proposes autonomic or organic computing principles to be applied to hardware design methods for future SoC solutions. Incorporating self-calibration, fault tolerance or even self-healing concepts into integrated circuit systems represents a major conceptual shift, which requires new design processes and tools. In the future, guarantee of functional correctness at the chip level includes self configuration of adaptable components and flexible interfaces supporting a flexible component composition within complex SoC systems
A 2001 IBM manifesto observed that a looming software complexity crisis -caused by applications and environments that number into the tens of millions of lines of code - threatened to halt progress in computing. The manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet. Autonomic computing, perhaps the most attractive approach to solving this problem, creates systems that can manage themselves when given high-level objectives from administrators. Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems.
Specialized elements of hardware and software, connected by wires, radio waves and infrared, will be so ubiquitous that no one will notice their presence.
Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and
following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that
generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components.
Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers
and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique
for tackling a wide variety of computationally hard problems.
Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.