Citation Link: https://doi.org/10.25819/ubsi/10999
Time-Triggered Organic Computing
Alternate Title
Zeitgesteuertes organisches Computing
Publication Type
Doctoral Thesis
Author(s)
Qosja, Mario
Date Issued
2026-06-10
Abstract
The complexity of embedded computing systems is growing due to applications with increasing functional and non-functional requirements, including performance, reliability, and energy efficiency. Organic Computing (OC) has emerged as a research area for handling complex distributed embedded computer systems and is inspired by the organization of biological entities.
In biology, Deoxyribonucleic Acid (DNA) is responsible for the structure and organization of living organisms.
The same concept can be used in embedded systems, where a single computer file, known as Artificial DNA (ADNA), contains information for the whole system (e.g., sensors, actuators). In addition, following the hormone system in mammals, an Artificial Hormone System (AHS) has been developed that has the ability to read the ADNA and self-organize the system by exchanging hormones.
It provides improved adaptability, robustness, and reduces development efforts. However, organic computing has drawbacks in terms of determinism, composability, and dependability, which are key features for safety-critical applications. Distributed computing systems using time-triggered communication networks have these characteristics and therefore offer distinct advantages for safety-critical systems. By combining organic computing technologies, such as artificial DNA and hormone features, with time-triggered communications, we can make these systems more adaptive, safe, reliable, and suitable for safety-critical applications while also providing self-x properties.
Therefore, in this thesis, we present a Time-Triggered Organic Computing architecture (TTOC).
In TTOC, computing nodes, also known as Processing Elements (PEs), exchange hormones with each other in order to perform the spatial allocation of application tasks. After a task is allocated, the chosen PE utilises a list-based scheduling algorithm that is designed specifically according to TTOC requirements to calculate the system schedule dynamically. The list-based algorithm calculates the temporal allocation of application tasks together with the spatial and temporal assignments of application messages.
The task dispatcher is responsible for the execution of application tasks and messages as defined by the time-triggered schedule. In the case of a PE failure, the remaining active nodes will switch to a reconfiguration state where the execution of the affected task is halted. The system recomputes the application graph, and the AHS starts the process of hormone exchange to determine task allocation based on the newly computed graph. The system will return to a normal state when all the affected tasks from the PE failure have been reallocated, and new schedules are generated.
To validate our proposed solution, an environment consisting of network and computation simulations is constructed. In addition, a co-simulation controller has been developed to couple both simulations.
The results obtained from the evaluation of the use cases show improved temporal predictability and fault containment of the system.
In biology, Deoxyribonucleic Acid (DNA) is responsible for the structure and organization of living organisms.
The same concept can be used in embedded systems, where a single computer file, known as Artificial DNA (ADNA), contains information for the whole system (e.g., sensors, actuators). In addition, following the hormone system in mammals, an Artificial Hormone System (AHS) has been developed that has the ability to read the ADNA and self-organize the system by exchanging hormones.
It provides improved adaptability, robustness, and reduces development efforts. However, organic computing has drawbacks in terms of determinism, composability, and dependability, which are key features for safety-critical applications. Distributed computing systems using time-triggered communication networks have these characteristics and therefore offer distinct advantages for safety-critical systems. By combining organic computing technologies, such as artificial DNA and hormone features, with time-triggered communications, we can make these systems more adaptive, safe, reliable, and suitable for safety-critical applications while also providing self-x properties.
Therefore, in this thesis, we present a Time-Triggered Organic Computing architecture (TTOC).
In TTOC, computing nodes, also known as Processing Elements (PEs), exchange hormones with each other in order to perform the spatial allocation of application tasks. After a task is allocated, the chosen PE utilises a list-based scheduling algorithm that is designed specifically according to TTOC requirements to calculate the system schedule dynamically. The list-based algorithm calculates the temporal allocation of application tasks together with the spatial and temporal assignments of application messages.
The task dispatcher is responsible for the execution of application tasks and messages as defined by the time-triggered schedule. In the case of a PE failure, the remaining active nodes will switch to a reconfiguration state where the execution of the affected task is halted. The system recomputes the application graph, and the AHS starts the process of hormone exchange to determine task allocation based on the newly computed graph. The system will return to a normal state when all the affected tasks from the PE failure have been reallocated, and new schedules are generated.
To validate our proposed solution, an environment consisting of network and computation simulations is constructed. In addition, a co-simulation controller has been developed to couple both simulations.
The results obtained from the evaluation of the use cases show improved temporal predictability and fault containment of the system.
License
Creative Commons license
File(s)![Thumbnail Image]()
Loading...
Name
Dissertation_Qosja_Mario.pdf
Size
17.53 MB
Format
Adobe PDF
Checksum
(MD5):3f2c22ab610eb49e38b42f297ac10b21
Owning collection
Mapped collections