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Explicit Allocation Strategy with Deadline and Budget Constraint Algorithm in Bag of Tasks Grid | Science Publications

机译:截止日期和预算约束算法在任务网格中的显式分配策略科学出版物

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> Problem statement: This study is for effective scheduling of grid jobs based on economy for space shared resources in Bag of tasks grid. Grid Computing aims in combining the power of heterogeneous, geographically distributed, multi-domain computational resources to provide high performance or high throughput. Approach: Space shared resources are parallel supercomputers and clusters of workstations that provides a great amount of computational power. These resources require jobs to be specified formally in terms of the amount of time (tr) and number of processors (p) needed for execution. Bag-of-Tasks (BoT) is an application consists of several uniprocessor and independent tasks that have no inter-task communications or task-dependencies. BoT is highly suitable for execution in grids. It is capable of tolerating network delays or faults and does not require formal job submission. The Explicit allocation strategy assigns the formal job parameters (p, tr) to the job requests, minimizing the overhead on the grid users to provide a formal job specification. This strategy uses adaptive heuristics to determine the parameters based on certain heuristics, in order to improve throughput. In the proposed system, explicit allocation strategy combined with Deadline and Budget Constraint (DBC) Cost Time optimization algorithm performs effective scheduling of the jobs based on the user?s quality of service (QoS) requirements such as deadline, budget and optimization strategy. Results: The cost-time optimization scheduling allocates the cheapest resources to ensure that the deadline can be met and computation is minimized. In case if there are two resources with the same cost, scheduling is done in any affordable resource so that the job gets executed as early as possible. Conclusion: The performance of this scheme against the existing system is evaluated using cost factor (Cfactor) and speed up ratio (Tspeedup) and this scheme is more effective than the existing system.
机译: > 问题陈述:该研究用于基于经济性有效地调度任务袋中的空间共享资源的网格作业。网格计算旨在结合异构,地理分布的多域计算资源的功能,以提供高性能或高吞吐量。 方法:空间共享资源是并行的超级计算机和工作站集群,可提供大量的计算能力。这些资源要求根据执行所需的时间(tr)和处理器数量(p)来正式指定作业。任务袋(BoT)是一个应用程序,由多个单处理器任务和独立任务组成,没有任务间通信或任务相关性。 BoT非常适合在网格中执行。它能够容忍网络延迟或故障,并且不需要正式的作业提交。显式分配策略将正式的作业参数(p,tr)分配给作业请求,从而最大程度地减少了网格用户提供正式作业规范的开销。该策略使用自适应启发式算法基于某些启发式算法来确定参数,以提高吞吐量。在提出的系统中,显式分配策略结合了截止日期和预算约束(DBC)成本时间优化算法,可以根据用户的服务质量(QoS)要求(例如截止日期,预算和优化策略)对作业进行有效的调度。 结果:成本时间优化计划分配了最便宜的资源,以确保能够满足期限并最大程度地减少计算量。如果有两个成本相同的资源,则在任何可负担的资源中进行调度,以使作业尽早执行。 结论:使用成本因子(C factor )和加速比(T speedup )评估该方案相对于现有系统的性能,并该方案比现有系统更有效。

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