UML Assisted Visual Debugging for Distributed Systems

The DOD is developing a Joint Battlespace Infosphere, linking a large number of data sources and user applications. To assist in this process, debugging and analysis tools are required. Software debugging is an extremely difficult cognitive process requiring comprehension of the overall application behavior, along with detailed understanding of specific application components. This is further complicated with distributed systems by the addition of other programs, their large size and synchronization issues. Typical debuggers provide inadequate support for this process, focusing primarily on the details accessible through source code. To overcome this deficiency, this research links the dynamic program execution state to a Unified Modeling Language (UML) class diagram that is reverse-engineered from data accessed within the Java Platform Debug Architecture. This research uses focus + context, graph layout, and color encoding techniques to enhance the standard UML diagram. These techniques organize and present objects and events in a manner that facilitates analysis of system behavior. High-level abstractions commonly used in system design support debugging while maintaining access to low-level details with an interactive display. The user is also able to monitor the control flow through highlighting of the relevant object and method in the display

Interactive Visual Analysis of Networked Systems: Workflows for Two Industrial Domains

We report on a first study of interactive visual analysis of networked systems. Working with ABB Corporate Research and Ericsson Research, we have created workflows which demonstrate the potential of visualization in the domains of industrial automation and telecommunications. By a workflow in this context, we mean a sequence of visualizations and the actions for generating them. Visualizations can be any images that represent properties of the data sets analyzed, and actions typically either change the selection of data visualized or change the visualization by choice of technique or change of parameters

ReSHAPE: A Framework for Dynamic Resizing and Scheduling of Homogeneous Applications in a Parallel Environment

Applications in science and engineering often require huge computational resources for solving problems within a reasonable time frame. Parallel supercomputers provide the computational infrastructure for solving such problems. A traditional application scheduler running on a parallel cluster only supports static scheduling where the number of processors allocated to an application remains fixed throughout the lifetime of execution of the job. Due to the unpredictability in job arrival times and varying resource requirements, static scheduling can result in idle system resources thereby decreasing the overall system throughput. In this paper we present a prototype framework called ReSHAPE, which supports dynamic resizing of parallel MPI applications executed on distributed memory platforms. The framework includes a scheduler that supports resizing of applications, an API to enable applications to interact with the scheduler, and a library that makes resizing viable. Applications executed using the ReSHAPE scheduler framework can expand to take advantage of additional free processors or can shrink to accommodate a high priority application, without getting suspended. In our research, we have mainly focused on structured applications that have two-dimensional data arrays distributed across a two-dimensional processor grid. The resize library includes algorithms for processor selection and processor mapping. Experimental results show that the ReSHAPE framework can improve individual job turn-around time and overall system throughput.Comment: 15 pages, 10 figures, 5 tables Submitted to International Conference on Parallel Processing (ICPP'07

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS