Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
This paper describes recent developments of the\nSiebog agent middleware regarding performance. This middleware\nsupports both server-side and client-side agents.\nServer side agents exist as EJB session beans on the JavaEE\napplication server, while client-side agents exist as JavaScript\nWorker objects in the browser. Siebog employs enterprise\ntechnologies on the server side to provide automatic agent\nload-balancing and fault-tolerance. Onthe client side this distributed\narchitecture relies on HTML5 and related standards\nto support smooth running on a wide variety of hardware\nand software platforms. Such architecture supports rather\neasy, reliable and efficient communication, interaction, and\ncoexistence between numerous agents. With the automatic\nclustering and state persistence, Siebog can support thousands\nof server-side agents, as well as thousands of external\ndevices hosting tens of client-side agents. Performed and presented\nexperiments showed promising results for real life\napplications of our architecture....
Autonomic software recovery enables software to automatically detect and recover software\nfaults. This feature makes the software to run more efficiently, actively, and reduces the maintenance\ntime and cost. This paper proposes an automated approach for Software Fault Detection and\nRecovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as\ncomponent deletion, replacement or modification, and recovers the component to enable the\nsoftware to continue its intended operation. The SFDR is analyzed and implemented in parallel as\na standalone software at the design phase of the target software. The practical applicability of the\nproposed approach has been tested by implementing an application demonstrating the performance\nand effectiveness of the SFDR. The experimental results and the comparisons with other\nworks show the effectiveness of the proposed approach....
In this paper, the well-known Cholesky Algorithm (for solving simultaneous linear\nequations, or SLE) is re-visited, with the ultimate goal of developing a simple, userfriendly,\nattractive, and useful Java Visualization and Animation Graphical User Interface\n(GUI) software as an additional teaching tool for students to learn the Cholesky\nfactorization in a step-by-step fashion with computer voice and animation. A\ndemo video of the Cholesky Decomposition (or factorization) animation and result\ncan be viewed online from the website:\nhttp://www.lions.odu.edu/~imako001/cholesky/demo/index.html. The software tool\ndeveloped from this work can be used for both students and their instructors not\nonly to master this technical subject, but also to provide a dynamic/valuable tool for\nobtaining the solutions for homework assignments, class examinations, self-assessment\nstudies, and other coursework related activities. Various transportation engineering\napplications of SLE are cited. Engineering educators who have adopted\nââ?¬Å?flipped class-room instructionââ?¬Â can also utilize this Java Visualization and Animation\nsoftware for students to ââ?¬Å?self-learningââ?¬Â these algorithms at their own time (and\nat their preferable locations), and use valuable class-meeting time for more challenging\n(real-life) problemsââ?¬â?¢ discussions. Statistical data for comparisons of studentsââ?¬â?¢\nperformanc...
IoT technologies are being rapidly adopted for manufacturing automation, remote\nmachine diagnostics, prognostic health management of industrial machines and\nsupply chain management. A recent on-demand model of manufacturing that is leveraging\nIoT technologies is called Cloud-Based Manufacturing. We propose a Software-\nDefined Industrial Internet of Things (SD-IIoT) platform for as a key enabler\nfor cloud-manufacturing, allowing flexible integration of legacy shop floor equipment\ninto the platform. SD-IIoT enables access to manufacturing resources and allows\nexchange of data between industrial machines and cloud-based manufacturing\napplications....
Due to rapid development in software industry, it was necessary to reduce time and efforts in the\nsoftware development process. Software Reusability is an important measure that can be applied\nto improve software development and software quality. Reusability reduces time, effort, errors,\nand hence the overall cost of the development process. Reusability prediction models are established\nin the early stage of the system development cycle to support an early reusability assessment.\nIn Object-Oriented systems, Reusability of software components (classes) can be obtained\nby investigating its metrics values. Analyzing software metric values can help to avoid developing\ncomponents from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order\nto identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to\ncluster datasets of CK metrics values that were extracted from three different java-based systems.\nThe goal was to find the relationship between CK metrics values and the reusability level of the\nclass. The reusability level of the class was classified into three main categorizes (High Reusable,\nMedium Reusable and Low Reusable). The clustering was based on metrics threshold values that\nwere used to achieve the experiments. The proposed methodology succeeds in classifying classes\nto their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments\nshow how SOM can be applied on software CK metrics with different sizes of SOM grids to provide\ndifferent levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and\nNumber of Children (NOC) metrics dominated the clustering process, so these two metrics were\ndiscarded from the experiments to achieve a successful clustering. The most efficient SOM topology\n[2 Ã?â?? 2] grid size is used to predict the reusability of classes....
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