SORT 2014

Abstract

The increasing complexity of contemporary embedded computing systems requires the use of self-management in order to handle unforeseen changes in both hardware and application environments (i.e., hardware/software defects, resource changes, and non-continual feature usage). Moreover, often these systems are distributed, running on processor architectures with multiple cores, which may require self-organization to ensure efficiency and reliability. Real-time properties are another key issue in many complex systems. Adaptive and self-organized properties extent the area of operations and improves the efficiency of the system resources at the cost to introduce additional complexity, overhead, and resource requirements. Consequently, real-time adaptive systems must be careful analyzed, designed, and built taken into account the right tradeoffs between flexibility and complexity, while accomplishing time-constrains. The combination of the flexibility and uncertain behavior of self-organizing systems with time-predictability is a grand challenge. Therefore, substantial research has been done in the last years to address the so-called Self-X features (e.g., self-configuration, self-optimization, self-adaptation, self-healing, and self-protection). This fact has as resutl that self-organizing computing systems become an established research nowadays as they promise to handle the increasing complexity resulting from highly distributed systems and ubiquitous applications. In addition, real-time properties are required in many areas (such as cyber physical systems) self-organizing computing systems are dealing with. Combining the flexible and and uncertain behavior of self-organizing systems with time-predictability necessary for real-time systems is a grand challenge. The Workshop on Self-Organizing Real-Time Systems (SORT) is specifically dedicated to research on adaptive real-time systems. SORT started 2014 as a workshop attached at International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC). The purpose of this workshop is to provide an open forum to discuss new and ongoing research that is centered on the idea of adaptability in real-time systems. The target audience includes researchers from academia, tool vendors, system suppliers, and users in industry who are interested in the all aspects of the topics mentioned below. This special issue of Concurrency and Computation: Practice and Experience contains four invited papers from the SORT 2014 workshop that has been expanded and carefully peer reviewed. The first paper, titled An Artificial DNA for Self-Descripting and Self-Building Embedded Real-Time Systems 1, Uwe Brinkschulte proposes an approach to use an artificial DNA-based approach for embedded real-time and distributed systems. This kind of systems is growing more and more complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields (e.g., in cars and in households). Bio-inspired techniques like self-organization are a key feature to handle this complexity. Because many embedded systems can be composed from a limited number of basic elements, the structure and parameters of such systems can be stored in a compact way representing an artificial DNA deposited in each computation node. This leads to a self-describing system. Based on the DNA, the self-organization mechanisms can build the system autonomously providing a selfbuilding system. System repair and optimization at runtime are also possible, leading to higher robustness, dependability, and flexibility. Autonomous adaptation in self-adapting embedded real-time systems introduces novel risks as it may lead to unforeseen system behavior. An anomaly detection framework integrated in a real-time operating system can ease the identification of such suspicious novel behavior and, thereby, offers the potential to enhance the reliability of the considered self-x system. However, anomaly detection is based on knowledge about normal behavior. When dealing with self-reconfiguring applications, normal behavior changes. Hence, knowledge base requires adaptation or even reconstruction at runtime. The stringent restrictions of real-time systems considering runtime and memory consumption make this task to a really challenging problem. In next paper, Two-Level Extensions of an Artifical Hormone System 2, Mathias Pacher describes a decentralized software which is able to allocate tasks in a system of heterogeneous processing elements. Tasks are allocated according to their suitability for the heterogeneous processing elements, the current processing element and task relationships. This software provides properties like self-configuration, self-optimization, and self-healing in the context of task allocation. In addition, it is able to guarantee real-time bounds for such self-X-properties. However, using self-organization principles introduces increased system complexity such as control of system parameters for self-organization and additional communication effort, which have been addressed by using a hierarchic structure. This solution uses a machine learning approach presenting an Observer-/Controller architecture. The user has to provide a simple set of initial rules and the Observer-/Controller is able to generate new rules if needed. This paper also presents a hierarchical structure to save communication bandwidth, which consists of several different clusters of processing elements where each cluster has its own communication infrastructure (e.g., a bus system). In the paper titled Online behavior classification for anomaly detection in self-x real-time systems 3, Katharina Stahl presents an online construction of application behavior knowledge that does not rely on training phase. The applications' behavior is defined by the application's system call invocations. For the knowledge base, they use Suffix Trees to represent application behavior patterns and associated information in a compact manner. The online algorithm provided by Suffix Trees is a basis to construct the knowledge base with low computational effort. Anomaly detection and classification is integrated into the online construction method. New behavioral patterns do not unconditionally update the behavior knowledge base. They are evaluated in a context-related manner inspired by Danger Theory, a special discipline of Artificial Immune Systems. For highly safety-critical applications, rigorous offline verification should be complemented by online verification. One promising technique is Online Model Checking (OMC). As OMC is a run- time-provided service, it seems to be natural providing it by an operating system service like any other service offered by the OS. In the paper titled Efficient Integration of Online Model Checking into a Small-Footprint Real-time Operating System 4 the authors study the feasibility of integrating OMC as an RTOS service. In order to ease understanding the approach, the paper discusses various integration methods in which OMC runs concurrently to the application task to be online model checked. The OMC may become: (i) an integral part of the RTOS, (ii) a separate task running on the same host as the RTOS, or (iii) a remote host as a kind of service-oriented architecture.