Browsing by Author "Wang, Frank Zhigang"

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    LAG: Achieving transparent access to legacy data by leveraging grid environment
    (Elsevier Science B.V., Amsterdam., 2011-01-31T00:00:00Z) Deng, Yuhui; Wang, Frank Zhigang
    The world today is experiencing an explosive growth of data generated by information digitization. Due to the unprecedented advance in software and hardware, large amounts of data gradually becomes legacy data and inaccessible. This is building a digital black hole, and it is becoming a big challenge to access, process, and preserve the legacy data. Grid provides flexible, secure, and coordinated resource sharing among dynamic collections of individuals, institutions, and resources. It allows users and applications to access the aggregated resources in a transparent manner. This paper proposes a Legacy Application Grid (LAG) architecture. This architecture deploys diverse legacy applications in a grid environment and provides a transparent access to the remote LAG users who want to access the legacy data. In contrast to the existing methods which attempt to tackle legacy data and legacy applications, we wrap a display protocol into grid services. The service provider, who wants to deploy any legacy applications, just needs to deploy the protocol based grid service, describe and pass the parameters of those legacy applications to the service. Compared with the traditional approaches, the method proposed in this paper is very cost-effective because it avoids converting legacy data from one format to another format or upgrading legacy applications one by one. An implemented prototype validates that the LAG architecture trades acceptable performance degradation for a transparent and remote access to legacy data. (C) 2010 Elsevier B.V. All rights reserved.
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    Network modelling and simulation tools
    (Elsevier, 2009-07) Rahman, Muhammad Azizur; Pakstas, Algirdas; Wang, Frank Zhigang
    Computer network technologies have been growing explosively and the study in computer networks is being a challenging task. To make this task easy, different users, researchers and companies have developed different network modelling and simulation (MS) tools. These network MS tools can be used in education and research as well as practical purposes. They vary with their characteristics. This paper reviews some of the most important network MS tools developed recently. This paper also shows a classification of the tools used in communications networks.
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    ItemOpen Access
    Transaction-filtering data mining and a predictive model for intelligent data management
    (Cranfield University, 2008-11) Liao, ChenHan; Wang, Frank Zhigang
    This thesis, first of all, proposes a new data mining paradigm (transaction-filtering association rule mining) addressing a time consumption issue caused by the repeated scans of original transaction databases in conventional associate rule mining algorithms. An in-memory transaction filter is designed to discard those infrequent items in the pruning steps. This filter is a data structure to be updated at the end of each iteration. The results based on an IBM benchmark show that an execution time reduction of 10% - 19% is achieved compared with the base case. Next, a data mining-based predictive model is then established contributing to intelligent data management within the context of Centre for Grid Computing. The capability of discovering unseen rules, patterns and correlations enables data mining techniques favourable in areas where massive amounts of data are generated. The past behaviours of two typical scenarios (network file systems and Data Grids) have been analyzed to build the model. The future popularity of files can be forecasted with an accuracy of 90% by deploying the above predictor based on the given real system traces. A further step towards intelligent policy design is achieved by analyzing the prediction results of files’ future popularity. The real system trace-based simulations have shown improvements of 2-4 times in terms of data response time in network file system scenario and 24% mean job time reduction in Data Grids compared with conventional cases.