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STEADY-STATE SCHEDULING OF MULTIPLE DIVISIBLE LOAD APPLICATIONS ON WIDE-AREA DISTRIBUTED COMPUTING PLATFORMS

机译:广域分布计算平台上多种可分负荷应用的稳态调度

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Divisible load applications consist of an amount of data and associated computation that can be divided arbitrarily into any number of independent pieces. This model is a good approximation of many real-world scientific applications, lends itself to a natural master-worker implementation, and has thus received a lot of attention. The critical issue of divisible load scheduling has been studied extensively in previous work. However, only a few authors have explored the simultaneous scheduling of multiple such applications on a distributed computing platform. We focus on this increasingly relevant scenario and make the following contributions. We use a novel and more realistic platform model that captures some of the fundamental network properties of grid platforms. We formulate the steady-state multi-application scheduling problem as a linear program that expresses a notion of fairness between applications. This scheduling problem is NP-complete and we propose several heuristics that we evaluate and compare via extensive simulation experiments. Our main finding is that some of our heuristics can achieve performance close to the optimal and we quantify the trade-offs between achieved performance and heuristic complexity.
机译:可分割的负载应用程序包含大量数据和相关计算,这些数据和相关计算可以任意划分为任意数量的独立片段。该模型很好地逼近了许多实际的科学应用,使其适合于自然的主人工实施,因此受到了广泛的关注。可分负荷调度的关键问题在先前的工作中已得到广泛研究。但是,只有少数作者探讨了在分布式计算平台上同时调度多个此类应用程序的问题。我们将重点放在这种日益相关的方案上,并做出以下贡献。我们使用一种新颖且更现实的平台模型,该模型捕获了网格平台的一些基本网络属性。我们将稳态多应用程序调度问题表达为一个线性程序,该程序表达了应用程序之间的公平性概念。这个调度问题是NP完全的,我们提出了几种启发式方法,我们通过大量的模拟实验进行评估和比较。我们的主要发现是,我们的某些启发式方法可以使性能接近最佳值,并且我们量化了所取得的性能与启发式复杂度之间的权衡。

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