Towards Automotive Embedded Systems with Self-X Properties

With self-adaptation and self-organization new paradigms for the management of distributed systems have been introduced. By enhancing the automotive software system with self-X capabilities, e.g. self-healing, self-configuration and self-optimization, the complexity is handled while increasing the flexibility, scalability and dependability of these systems. In this chapter we present an approach for enhancing automotive systems with self-X properties. At first, we discuss the benefits of providing automotive software systems with self-management capabilities and outline concrete use cases. Afterwards, we will discuss requirements and challenges for realizing adaptive automotive embedded systems

09201 Abstracts Collection -- Self-Healing and Self-Adaptive Systems

From May 10th 2009 to May 15th 2009 the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar are put together in this paper. Links to extended abstracts or full papers are provided, if available. A description of the seminar topics, goals and results in general can be found in a separate document ``Executive Summary\u27\u27

08141 Abstracts Collection -- Organic Computing - Controlled Self-organization

From March 30th to April 4th 2008, the Dagstuhl Seminar 08141 "Organic Computing - Controlled Self-organization"\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters

Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes the appearance of a new spectrum of services with sophisticated workload and resource management requirements. Also, data centers are growing by addition of various type of hardware to accommodate the ever-increasing requests of users. Nowadays a large percentage of cloud resources are executing data-intensive applications which need continuously changing workload fluctuations and specific resource management. To this end, cluster computing frameworks are shifting towards distributed resource management for better scalability and faster decision making. Such systems benefit from the parallelization of control and are resilient to failures. Throughout this thesis we investigate algorithms, protocols and techniques to address these challenges in large-scale data centers. We introduce a distributed resource management framework which consolidates virtual machine to as few servers as possible to reduce the energy consumption of data center and hence decrease the cost of cloud providers. This framework can characterize the workload of virtual machines and hence handle trade-off energy consumption and Service Level Agreement (SLA) of customers efficiently. The algorithm is highly scalable and requires low maintenance cost with dynamic workloads and it tries to minimize virtual machines migration costs. We also introduce a scalable and distributed probe-based scheduling algorithm for Big data analytics frameworks. This algorithm can efficiently address the problem job heterogeneity in workloads that has appeared after increasing the level of parallelism in jobs. The algorithm is massively scalable and can reduce significantly average job completion times in comparison with the-state of-the-art. Finally, we propose a probabilistic fault-tolerance technique as part of the scheduling algorithm

Pervasive computing reference architecture from a software engineering perspective (PervCompRA-SE)

Pervasive computing (PervComp) is one of the most challenging research topics nowadays. Its complexity exceeds the outdated main frame and client-server computation models. Its systems are highly volatile, mobile, and resource-limited ones that stream a lot of data from different sensors. In spite of these challenges, it entails, by default, a lengthy list of desired quality features like context sensitivity, adaptable behavior, concurrency, service omnipresence, and invisibility. Fortunately, the device manufacturers improved the enabling technology, such as sensors, network bandwidth, and batteries to pave the road for pervasive systems with high capabilities. On the other hand, this domain area has gained an enormous amount of attention from researchers ever since it was first introduced in the early 90s of the last century. Yet, they are still classified as visionary systems that are expected to be woven into people’s daily lives. At present, PervComp systems still have no unified architecture, have limited scope of context-sensitivity and adaptability, and many essential quality features are insufficiently addressed in PervComp architectures. The reference architecture (RA) that we called (PervCompRA-SE) in this research, provides solutions for these problems by providing a comprehensive and innovative pair of business and technical architectural reference models. Both models were based on deep analytical activities and were evaluated using different qualitative and quantitative methods. In this thesis we surveyed a wide range of research projects in PervComp in various subdomain areas to specify our methodological approach and identify the quality features in the PervComp domain that are most commonly found in these areas. It presented a novice approach that utilizes theories from sociology, psychology, and process engineering. The thesis analyzed the business and architectural problems in two separate chapters covering the business reference architecture (BRA) and the technical reference architecture (TRA). The solutions for these problems were introduced also in the BRA and TRA chapters. We devised an associated comprehensive ontology with semantic meanings and measurement scales. Both the BRA and TRA were validated throughout the course of research work and evaluated as whole using traceability, benchmark, survey, and simulation methods. The thesis introduces a new reference architecture in the PervComp domain which was developed using a novel requirements engineering method. It also introduces a novel statistical method for tradeoff analysis and conflict resolution between the requirements. The adaptation of the activity theory, human perception theory and process re-engineering methods to develop the BRA and the TRA proved to be very successful. Our approach to reuse the ontological dictionary to monitor the system performance was also innovative. Finally, the thesis evaluation methods represent a role model for researchers on how to use both qualitative and quantitative methods to evaluate a reference architecture. Our results show that the requirements engineering process along with the trade-off analysis were very important to deliver the PervCompRA-SE. We discovered that the invisibility feature, which was one of the envisioned quality features for the PervComp, is demolished and that the qualitative evaluation methods were just as important as the quantitative evaluation methods in order to recognize the overall quality of the RA by machines as well as by human beings