Conference Paper

Grid allocation and reservation - Grid capacity planning with negotiation-based advance reservation for optimized QoS.

Conference: Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing, November 11-17, 2006, Tampa, FL, USA
Source: DBLP
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    ABSTRACT: We consider the problem of providing QoS guarantees to Grid users through advance reservation of resources. Advance reservation mechanisms provide the ability to allocate resources to users based on agreed-upon QoS require-ments and increase the predictability of a Grid system, yet incorporating such mechanisms into current Grid environments has proven to be a chal-lenging task due to the resulting resource fragmentation. We use concepts from computational geometry to present a framework for tackling the re-source fragmentation, and for formulating a suite of scheduling strategies. We also develop efficient implementations of the scheduling algorithms that scale to large Grids. We conduct a comprehensive performance evaluation study using simulation, and we present numerical results to demonstrate that our strategies perform well across several metrics that reflect both user-and system-specific goals. Our main contribution is a timely, practical, and efficient solution to the problem of scheduling resources in emerging on-demand computing environments.
    J. Parallel Distrib. Comput. 01/2011; 71.
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    ABSTRACT: Growth in availability of data collection devices has allowed individual researchers to gain access to large quantities of data that needs to be analyzed. As a result, many labs and departments have acquired considerable compute resources. However, effective and efficient utilization of those resources remains a barrier for the individual researchers because the distributed computing environments are difficult to understand and control. We introduce a methodology and a tool that automatically manipulates and understands job submission parameters to realize a range of job execution alternatives across a distributed compute infrastructure. Generated alternatives are presented to a user at the time of job submission in the form of tradeoffs mapped onto two conflicting objectives, namely job cost and runtime. Such presentation of job execution alternatives allows a user to immediately and quantitatively observe viable options regarding their job execution, and thus allows the user to interact with the environment at a true service level. Generated job execution alternatives have been tested through simulation and on real-world resources and, in both cases, the average accuracy of the runtime of the generated and perceived job alternatives is within 5%.
    The Journal of Supercomputing 01/2012; 59:1431-1454. · 0.92 Impact Factor
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    ABSTRACT: One of the key motivations of computational and data Grids is the ability to make coordinated use of heterogeneous computing resources which are geographically dispersed. However, the provision of Quality of Service (QoS) to Grid users is still a challenge that needs the attention of the research community. Reservation of resources in advance has been proposed as a way of providing QoS guarantees but they may not always be possible. For this reason, this work focuses on meta-scheduling of jobs in advance as a way of enhancing the provision of QoS. Thereby, jobs are scheduled some time before they are actually executed, but no resource is physically reserved. One of the drawbacks of this scenario is that fragmentation may appear in resources (free time slots but not large enough to execute a job) which leads to poor resource utilization. For that reason, two techniques have been developed to tackle poor resource utilization, whose main idea consists of rescheduling already scheduled jobs so that a new incoming job can be allocated. These rescheduling techniques have been implemented within a middleware that supports meta-scheduling in advance, which relies on the GridWay meta-scheduler. Finally, these proposals have been tested using a real testbed involving heterogeneous computing resources distributed across different national organizations, with different experiments showing their efficiency.
    Journal of Grid Computing 08/2012; 10(3):475-499. · 1.60 Impact Factor