The role of the host in a cooperating mainframe and workstation environment, volumes 1 and 2

In recent years, advancements made in computer systems have prompted a move from centralized computing based on timesharing a large mainframe computer to distributed computing based on a connected set of engineering workstations. A major factor in this advancement is the increased performance and lower cost of engineering workstations. The shift to distributed computing from centralized computing has led to challenges associated with the residency of application programs within the system. In a combined system of multiple engineering workstations attached to a mainframe host, the question arises as to how does a system designer assign applications between the larger mainframe host and the smaller, yet powerful, workstation. The concepts related to real time data processing are analyzed and systems are displayed which use a host mainframe and a number of engineering workstations interconnected by a local area network. In most cases, distributed systems can be classified as having a single function or multiple functions and as executing programs in real time or nonreal time. In a system of multiple computers, the degree of autonomy of the computers is important; a system with one master control computer generally differs in reliability, performance, and complexity from a system in which all computers share the control. This research is concerned with generating general criteria principles for software residency decisions (host or workstation) for a diverse yet coupled group of users (the clustered workstations) which may need the use of a shared resource (the mainframe) to perform their functions

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

Autonomic Road Transport Support Systems

The work on Autonomic Road Transport Support (ARTS) presented here aims at meeting the challenge of engineering autonomic behavior in Intelligent Transportation Systems (ITS) by fusing research from the disciplines of traffic engineering and autonomic computing. Ideas and techniques from leading edge artificial intelligence research have been adapted for ITS over the last years. Examples include adaptive control embedded in real time traffic control systems, heuristic algorithms (e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated surveillance interpretation). Autonomic computing which is inspired from the biological example of the body’s autonomic nervous system is a more recent development. It allows for a more efficient management of heterogeneous distributed computing systems. In the area of computing, autonomic systems are endowed with a number of properties that are generally referred to as self-X properties, including self-configuration, self-healing, self-optimization, self-protection and more generally self-management. Some isolated examples of autonomic properties such as self-adaptation have found their way into ITS technology and have already proved beneficial. This edited volume provides a comprehensive introduction to Autonomic Road Transport Support (ARTS) and describes the development of ARTS systems. It starts out with the visions, opportunities and challenges, then presents the foundations of ARTS and the platforms and methods used and it closes with experiences from real-world applications and prototypes of emerging applications. This makes it suitable for researchers and practitioners in the fields of autonomic computing, traffic and transport management and engineering, AI, and software engineering. Graduate students will benefit from state-of-the-art description, the study of novel methods and the case studies provided

Adaptive Optimal Control of MapReduce Performance, Availability and Costs

International audienceMapReduce is a popular programming model for distributed data processing and Big Data applications running on clouds. Extensive research has been conducted either to improve the dependability or to increase performance of MapReduce, ranging from adaptive and on-demand fault-tolerance solutions, adaptive task scheduling techniques to optimized job execution mechanisms. This paper investigates an optimization-based solution to control MapReduce systems in order to provide guarantees in terms of both performance and availability while reducing utilization costs. We follow a control theoretical approach for MapReduce cluster scaling and admission control. Moreover, we aim to be robust to changes in MapRe-duce and in it's environment by adapting the controller online to those changes. This paper highlights the major challenges of combining system adaptation and optimal control to take the best of both approaches. CCS Concepts • Networks → Cloud computing; • Software and its engineering → Software configuration management and version control systems; • Computer systems organization → Dependable and fault-tolerant systems and networks

Construction of data streams applications from functional, non-functional and resource requirements for electric vehicle aggregators. the COSMOS vision

COSMOS, Computer Science for Complex System Modeling, is a research team that has the mission of bridging the gap between formal methods and real problems. The goal is twofold: (1) a better management of the growing complexity of current systems; (2) a high quality of the implementation reducing the time to market. The COSMOS vision is to prove this approach in non-trivial industrial problems leveraging technologies such as software engineering, cloud computing, or workflows. In particular, we are interested in the technological challenges arising from the Electric Vehicle (EV) industry, around the EV-charging and control IT infrastructure

Systematically Engineering Self-Organizing Systems: The SodekoVS Approach

Self-organizing systems promise new software quality attributes that are very hard to obtain using standard software engineering approaches. In accordance with the visions of e.g. autonomic computing and organic computing, self-organizing systems promote self-adaptability as one major property helping to realize software that can manage itself at runtime. In this respect, self-adaptability can be seen as a necessary foundation for realizing e.g. self* properties such as self-configuration or self-protection. However, the systematic development of systems exhibiting such properties challenges current development practices. The SodekoVS project addresses the challenge to purposefully engineer adaptivity by proposing a new approach that considers the system architecture as well as the software development methodology as integral intertwined aspects for system construction. Following the proposed process, self-organizing dynamics, inspired by biological, physical and social systems, can be integrated into applications by composing modules that distribute feedback control structures among system entities. These compositions support hierarchical as well as completely decentralized solutions without a single point of failure. This novel development conception is supported by a reference architecture, a tailored programming model as well as a library of ready to use self-organizing patterns. The key challenges, recent research activities, application scenarios as well as intermediate results are discussed

Modern software cybernetics: new trends

Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research

Modern software cybernetics: New trends

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Software cybernetics research is to apply a variety of techniques from cybernetics research to software engineering research. For more than fifteen years since 2001, there has been a dramatic increase in work relating to software cybernetics. From cybernetics viewpoint, the work is mainly on the first-order level, namely, the software under observation and control. Beyond the first-order cybernetics, the software, developers/users, and running environments influence each other and thus create feedback to form more complicated systems. We classify software cybernetics as Software Cybernetics I based on the first-order cybernetics, and as Software Cybernetics II based on the higher order cybernetics. This paper provides a review of the literature on software cybernetics, particularly focusing on the transition from Software Cybernetics I to Software Cybernetics II. The results of the survey indicate that some new research areas such as Internet of Things, big data, cloud computing, cyber-physical systems, and even creative computing are related to Software Cybernetics II. The paper identifies the relationships between the techniques of Software Cybernetics II applied and the new research areas to which they have been applied, formulates research problems and challenges of software cybernetics with the application of principles of Phase II of software cybernetics; identifies and highlights new research trends of software cybernetic for further research

Automotive embedded systems software reprogramming

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThe exponential growth of computer power is no longer limited to stand alone computing systems but applies to all areas of commercial embedded computing systems. The ongoing rapid growth in intelligent embedded systems is visible in the commercial automotive area, where a modern car today implements up to 80 different electronic control units (ECUs) and their total memory size has been increased to several hundreds of megabyte. This growth in the commercial mass production world has led to new challenges, even within the automotive industry but also in other business areas where cost pressure is high. The need to drive cost down means that every cent spent on recurring engineering costs needs to be justified. A conflict between functional requirements (functionality, system reliability, production and manufacturing aspects etc.), testing and maintainability aspects is given. Software reprogramming, as a key issue within the automotive industry, solve that given conflict partly in the past. Software Reprogramming for in-field service and maintenance in the after sales markets provides a strong method to fix previously not identified software errors. But the increasing software sizes and therefore the increasing software reprogramming times will reduce the benefits. Especially if ECU’s software size growth faster than vehicle’s onboard infrastructure can be adjusted. The thesis result enables cost prediction of embedded systems’ software reprogramming by generating an effective and reliable model for reprogramming time for different existing and new technologies. This model and additional research results contribute to a timeline for short term, mid term and long term solutions which will solve the currently given problems as well as future challenges, especially for the automotive industry but also for all other business areas where cost pressure is high and software reprogramming is a key issue during products life cycle