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A Constraint-based Decentralized Task Routing Approach to Large-Scale Scheduling in Cloud Environment

机译:云环境下基于约束的分散任务路由大规模调度方法

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This article mainly focused on cloud scheduling with constraint based decentralized task routing. The job of scheduling tasks across various nodes in a hierarchical network scenario is an exigent problem. The concept of decentralized distribution scheme proposed in base paper is time-consuming since it has to compute the availability function for each and every node. In this paper we proposed a CBDA (Constraint Based Decentralized Algorithm) which offers the expediency of being quick and “Make-span minimization policy” is implemented to reduce the completion time of the currently executing nodes. In our presumption, the submission nodes are semi centralized and it can store the availability information of the nodes or routers within its area. This paper considers the allotment of the tasks to the execution nodes which are unoccupied by other tasks. The dynamic allotment of the tasks to the nodes in the tree based approach is the major criteria for selecting the desired node. This paper proposes a trade-off between fully centralized model and the decentralized model by implementing a new constraint based decentralization scheme which saves time consumption and enhances efficiency of task scheduling.
机译:本文主要关注基于约束的分散任务路由的云调度。在分层网络方案中跨各个节点调度任务的工作是一个迫在眉睫的问题。原纸中提出的分散式分配方案的概念很耗时,因为它必须计算每个节点的可用性函数。在本文中,我们提出了一种CBDA(基于约束的分散算法),该算法提供了快速的便利性,并且实施了“跨度最小化策略”以减少当前执行的节点的完成时间。在我们的假设中,提交节点是半集中式的,它可以在其区域内存储节点或路由器的可用性信息。本文考虑了将任务分配给其他任务不占用的执行节点。将任务动态分配给基于树的方法是选择所需节点的主要标准。本文通过实现一种新的基于约束的分散方案,提出了在完全集中式模型和分散模型之间进行权衡的方法,该方案节省了时间并提高了任务调度的效率。

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