Simulating Resource and Service Trading in Grid Systems.
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Grid computing, one of the latest buzzwords in the ICT industry, is emerging as a new paradigm for Internet-based parallel and distributing computing. Despite a number of advances in grid computing, resource management and application scheduling in such environments continues to be a challenging and complex undertaking. This is due to geographic distribution of grid resources owned by different organizations with different usage policies, cost models and varying load and availability patterns with time. This tutorial introduces fundamental principles of grid computing and computational economy and discusses how they impact on emerging computational and data grid technologies. It identifies resource management challenges and introduces new challenges and requirements introduced by the grid economy on grid service providers (GSPs) and grid service consumers. The tutorial presents a service-oriented grid architecture inspired by computational economies and demonstrates how it can be realized by leveraging the existing grid technologies and building new economic-oriented capabilities and components. We present solutions to these challenges based on our experience in designing and developing market-oriented Gridbus technologies such as Grid Market Directory, Grid Bank, Grid Service Broker, Workflow Engine, and SLA-based enterprise Grid Resource Allocation system. Case studies on the use of Gridbus middleware in the creation of various e-science and e-business applications and their deployment on national/international utility-oriented grids along with its impact on emerging cloud computing paradigm will also be highlighted.
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To bridge the gap between computing grid environment and users, various Grid Widgets are developed by the Grid development team in the National Center for High-performance Computing (NCHC). These widgets are implemented to provide users with seamless and scalable access to Grid resources. Currently, this effort integrates the de facto Grid middleware, Web-based Operating System (WebOS), and automatic resource allocation mechanism to form a virtual computer in distributed computing environment. With the capability of automatic resource allocation and the feature of dynamic load prediction, the Resource Broker (RB) improves the performance of the dynamic scheduling over conventional scheduling policies. With this extremely lightweight and flexible approach to acquire Grid services, the barrier for users to access geographically distributed heterogeneous Grid resources is largely reduced. The Grid Widgets can also be customized and configured to meet the demands of the users.
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SGJOMS is an easy-to-use frontend for job management for the Seoul-Grid System which covers whole area of Seoul, the capital city of. It has been developed to meet the needs of Seoul-Grid Project and offers possibilities for extension and customization that make it interesting for a wide range of user communities. SGMOS allows users to do several standard actions in Grid job management. One of them is submission of Grid jobs and their monitoring. This functionality supports Grid users and developers to create and manage Grid jobs independent of the middleware. Grid Resource brokering facility, Grid file management facility, Grid job accounting facility and Grid resource monitoring facility for job management are provided by SGJOMS. In this paper, the internals of SGJOMS is explained.
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Grid is an emerging infrastructure which enables effective coordinate access to various distributed computing resources in order to serve the needs of collaborative research and work across the world. Grid resource management is always a key subject in the grid computing. We first analyze the resource management in the grid computing environment, then according to the load imbalance question in the ant colony optimization algorithm, propose an improved algorithm that suits to be used in the grid environment.
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In order to help endusers to manage their concerned resources easily in Grid system, one model based on deployment is proposed in this paper. By introducing deployment idea and related operations, our model can find out all the required ones from numerous resources in Grid system according to endusers' requirements. Under this model, one uniform management operation for resources is designed to provide transparent management so that endusers are shielded from the complicated details, and free to concentrate on their domain science.
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Because of exist model isn't obtain resource in more extension.An abstract model is proposed.The demanded resource is obtained in more extension using some interface to release.A prototype system for the model is constructed.Some computing study is executed on this grid.Finally the result proved this grid's feasibility.
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From the perspective of resource sharing, computational grid is a system ranging from small kind of network system for home using to large-scale network computing systems even to the Internet. The management of resources in the grid environment becomes very complex as these resources are distributed geographically, heterogeneous in nature, owned by different organizations and managing fields, and each having their own resource management policies and different access as well as cost models. In this paper computational grid resource management policies were analysised and discussed.
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Grid computing environments need secure resource control and predictable service quality in order to be sustainable. We propose a grid hosting model in which independent, self-contained grid deployments run within isolated containers on shared resource provider sites. Sites and hosted grids interact via an underlying resource control plane to manage a dynamic binding of computational resources to containers. We present a prototype grid hosting system, in which a set of independent Globus grids share a network of cluster sites. Each grid instance runs a coordinator that leases and configures cluster resources for its grid on demand. Experiments demonstrate adaptive provisioning of cluster resources and contrast job-level and container-level resource management in the context of two grid application managers.
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