Computing and Information Systems - Theses

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    QoS-based scheduling of workflows on global grids
    YU, JIA ( 2007-10)
    Grid computing has emerged as a global cyber-infrastructure for the next-generation of e-Science applications by integrating large-scale, distributed and heterogeneous resources. Scientific communities are utilizing Grids to share, manage and process large data sets. In order to support complex scientific experiments, distributed resources such as computational devices, data, applications, and scientific instruments need to be orchestrated while managing the application workflow operations within Grid environments. This thesis investigates properties of Grid workflow management systems, presents a workflow engine and algorithms for mapping scientific workflow applications to Grid resources based on specified QoS (Quality of Service) constraints. (For complete abstract open document)
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    Coordinated resource provisioning in federated grids
    RANJAN, RAJIV ( 2007-07)
    A fundamental problem in building large scale Grid resource sharing system is the need for efficient and scalable techniques for discovery and provisioning of resources for delivering expected Quality of Service (QoS) to users’ applications. The current approaches to Grid resource sharing based on resource brokers are non-coordinated since these brokers make scheduling related decisions independent of the others in the system. Clearly, this worsens the load-sharing and utilisation problems of distributed Grid resources as sub-optimal schedules are likely to occur. Further, existing brokering systems rely on centralised information services for resource discovery. Centralised or hierarchical resource discovery systems are prone to single-point failure, lack scalability and fault-tolerance ability. In the centralised model, the network links leading to the server are very critical to the overall functionality of the system, as their failure might halt the entire distributed system operation.
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    Scheduling distributed data-intensive applications on global grids
    VENUGOPAL, SRIKUMAR ( 2006-07)
    The next generation of scientific experiments and studies are being carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for such collaborations as it aids communities in sharing resources to achieve common objectives. Data Grids provide services for accessing, replicating and managing data collections in these collaborations. Applications used in such Grids are distributed data-intensive, that is, they access and process distributed datasets to generate results. These applications need to transparently and efficiently access distributed data and computational resources. This thesis investigates properties of data-intensive computing environments and presents a software framework and algorithms for mapping distributed data-oriented applications to Grid resources. (For complete abstract open document)
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    Meta scheduling for market-oriented grid and utility computing
    Garg, Saurabh Kumar ( 2010)
    Grid computing enables the sharing and aggregation of autonomous IT resources to deliver them as computing utilities to end users. The management of the Grid environment is a complex task as resources are geographically distributed, heterogeneous and autonomous in nature, and their users are self-interested. In utility-oriented Grids, users define their application requirements and compete to access the most efficient and cheapest resources. Traditional resource management systems and algorithms are based on system-centric approaches which do not take into account individual requirements and interests. To this end, market-oriented scheduling is an adequate way to solve the problem. But current market-oriented systems generally, either try to maximise one user’s utility or one provider’s utility. Such approaches fail to solve the problem of contention for cheap and efficient resources which may lead to unnecessary delays in job execution and underutilisation of resources. To address these problems, this thesis proposes a market-oriented meta-scheduler called “Meta-Broker”, which not only coordinates the resource demand but also allocates the best resources to users in terms of monetary and performance costs. The thesis results demonstrate that considerable cost reduction and throughput can be gained by adopting our proposed approach. The meta-broker has a semi-decentralised architecture, where only scheduling decisions are made by the meta-broker while job submission, execution and monitoring are delegated to user and provider middleware. This thesis also investigates market-oriented meta-scheduling algorithms which aim to maximise the utility of participants. The market-oriented algorithms consider Quality of Service (QoS) requirements of multiple users to map jobs against autonomous and heterogeneous resources. This thesis also presents a novel Grid Market Exchange architecture which provides the flexibility to users in choosing their own negotiation protocol for resource trading. The key research findings and contributions of this thesis are: - The consideration of QoS requirements of all users is necessary for maximising users’ utility and utilisation of resources. The uncoordinated scheduling of applications by personalised user-brokers leads to overloading of cheap and efficient resources. - It is important to exploit the heterogeneity between different resource sites/data centers while scheduling jobs to maximise the provider’s utility. This consideration not only reduce energy cost of computing infrastructure by 33% on average, but also enhance the efficiency of resources in terms of carbon emissions. - By considering both system metrics and market parameters, we can enable more effective scheduling which maximises the utility of both users and resource providers.