Design and implementation of a windows-based parallel computing environment for large scale optimization

A parallel computing environment to support optimization of large-scale engineering systems is designed and implemented on Windows-based personal computer networks, using the master-worker model and the Parallel Virtual Machine (PVM). It is involved in decomposition of a large engineering system into a number of smaller subsystems optimized in parallel on worker nodes and coordination of subsystem optimization results on the master node. The environment consists of six functional modules, i.e. the master control, the optimization model generator, the optimizer, the data manager, the monitor, and the post processor. Object-oriented design of these modules is presented. The environment supports steps from the generation of optimization models to the solution and the visualization on networks of computers. User-friendly graphical interfaces make it easy to define the problem, and monitor and steer the optimization process. It has been verified by an example of a large space truss optimization. (C) 2004 Elsevier Ltd. All rights reserved.

A novel grid computing approach for probabilistic small signal analysis

Grid computing is an advanced technique for collaboratively solving complicated scientific problems using geographically and organisational dispersed computational, data storage and other recourses. Application of grid computing could provide significant benefits to all aspects of power system that involves using computers. Based on our previous research, this paper presents a novel grid computing approach for probabilistic small signal stability (PSSS) analysis in electric power systems with uncertainties. A prototype computing grid is successfully implemented in our research lab to carry out PSSS analysis on two benchmark systems. Comparing to traditional computing techniques, the gird computing has given better performances for PSSS analysis in terms of computing capacity, speed, accuracy and stability. In addition, a computing grid framework for power system analysis has been proposed based on the recent study.

RSA-Grid: A grid computing based framework for power system reliability and security analysis

Grid computing is an emerging technology for providing the high performance computing capability and collaboration mechanism for solving the collaborated and complex problems while using the existing resources. In this paper, a grid computing based framework is proposed for the probabilistic based power system reliability and security analysis. The suggested name of this computing grid is Reliability and Security Grid (RSA-Grid). Then the architecture of this grid is presented. A prototype system has been built for further development of grid-based services for power systems reliability and security assessment based on probabilistic techniques, which require high performance computing and large amount of memory. Preliminary results based on prototype of this grid show that RSA-Grid can provide the comprehensive assessment results for real power systems efficiently and economically.

Active node supporting context-aware vertical handover in pervasive computing environment with redundant positioning

A major requirement for pervasive systems is to integrate context-awareness to support heterogeneous networks and device technologies and at the same time support application adaptations to suit user activities. However, current infrastructures for pervasive systems are based on centralized architectures which are focused on context support for service adaptations in response to changes in the computing environment or user mobility. In this paper, we propose a hierarchical architecture based on active nodes, which maximizes the computational capabilities of various nodes within the pervasive computing environment, while efficiently gathering and evaluating context information from the user's working environment. The migratable active node architecture employs various decision making processes for evaluating a rich set of context information in order to dynamically allocate active nodes in the working environment, perform application adaptations and predict user mobility. The active node also utilizes the Redundant Positioning System to accurately manage user's mobility. This paper demonstrates the active node capabilities through context-aware vertical handover applications.

Services on Application Level in Grid for Scientific Calculations

AMS Subj. Classification: 00-02, (General)

A study on the deployment of GA in a grid computing framework

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Simulation of computational grids scheduling politics

Computational grids allow users to share resources of distributed machines, even if those machines belong to different corporations. The scheduling of applications must be performed aiming at performance goals, and focusing on choose which processes can have access to specif resources, and which resources. In this article we discuss aspects of scheduling of application in grid computing environment. We also present a tool for scheduling simulation along with test scenarios and results.

Exploring distributed computing tools through data mining tasks

Harnessing idle PCs CPU cycles, storage space and other resources of networked computers to collaborative are mainly fixated on for all major grid computing research projects. Most of the university computers labs are occupied with the high puissant desktop PC nowadays. It is plausible to notice that most of the time machines are lying idle or wasting their computing power without utilizing in felicitous ways. However, for intricate quandaries and for analyzing astronomically immense amounts of data, sizably voluminous computational resources are required. For such quandaries, one may run the analysis algorithms in very puissant and expensive computers, which reduces the number of users that can afford such data analysis tasks. Instead of utilizing single expensive machines, distributed computing systems, offers the possibility of utilizing a set of much less expensive machines to do the same task. BOINC and Condor projects have been prosperously utilized for solving authentic scientific research works around the world at a low cost. In this work the main goal is to explore both distributed computing to implement, Condor and BOINC, and utilize their potency to harness the ideal PCs resources for the academic researchers to utilize in their research work. In this thesis, Data mining tasks have been performed in implementation of several machine learning algorithms on the distributed computing environment.

An efficient use of virtualization in grid/cloud environments

Grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational resources. Grid enables access to the resources but it does not guarantee any quality of service. Moreover, Grid does not provide performance isolation; job of one user can influence the performance of other user’s job. The other problem with Grid is that the users of Grid belong to scientific community and the jobs require specific and customized software environment. Providing the perfect environment to the user is very difficult in Grid for its dispersed and heterogeneous nature. Though, Cloud computing provide full customization and control, but there is no simple procedure available to submit user jobs as in Grid. The Grid computing can provide customized resources and performance to the user using virtualization. A virtual machine can join the Grid as an execution node. The virtual machine can also be submitted as a job with user jobs inside. Where the first method gives quality of service and performance isolation, the second method also provides customization and administration in addition. In this thesis, a solution is proposed to enable virtual machine reuse which will provide performance isolation with customization and administration. The same virtual machine can be used for several jobs. In the proposed solution customized virtual machines join the Grid pool on user request. Proposed solution describes two scenarios to achieve this goal. In first scenario, user submits their customized virtual machine as a job. The virtual machine joins the Grid pool when it is powered on. In the second scenario, user customized virtual machines are preconfigured in the execution system. These virtual machines join the Grid pool on user request. Condor and VMware server is used to deploy and test the scenarios. Condor supports virtual machine jobs. The scenario 1 is deployed using Condor VM universe. The second scenario uses VMware-VIX API for scripting powering on and powering off of the remote virtual machines. The experimental results shows that as scenario 2 does not need to transfer the virtual machine image, the virtual machine image becomes live on pool more faster. In scenario 1, the virtual machine runs as a condor job, so it easy to administrate the virtual machine. The only pitfall in scenario 1 is the network traffic.

A collaborative working environment for a large scale environmental model

Large scientific applications are usually developed, tested and used by a group of geographically dispersed scientists. The problems associated with the remote development and data sharing could be tackled by using collaborative working environments. There are various tools and software to create collaborative working environments. Some software frameworks, currently available, use these tools and software to enable remote job submission and file transfer on top of existing grid infrastructures. However, for many large scientific applications, further efforts need to be put to prepare a framework which offers application-centric facilities. Unified Air Pollution Model (UNI-DEM), developed by Danish Environmental Research Institute, is an example of a large scientific application which is in a continuous development and experimenting process by different institutes in Europe. This paper intends to design a collaborative distributed computing environment for UNI-DEM in particular but the framework proposed may also fit to many large scientific applications as well.

Scheduler simulation using iSPD, an iconic-based computer grid simulator

Increased accessibility to high-performance computing resources has created a demand for user support through performance evaluation tools like the iSPD (iconic Simulator for Parallel and Distributed systems), a simulator based on iconic modelling for distributed environments such as computer grids. It was developed to make it easier for general users to create their grid models, including allocation and scheduling algorithms. This paper describes how schedulers are managed by iSPD and how users can easily adopt the scheduling policy that improves the system being simulated. A thorough description of iSPD is given, detailing its scheduler manager. Some comparisons between iSPD and Simgrid simulations, including runs of the simulated environment in a real cluster, are also presented. © 2012 IEEE.