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Data-flow driven optimal tasks distribution for global heterogeneous systems

机译：数据流驱动的全局异构系统的最佳任务分布

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摘要

As a result of advances in technology and highly demanding users expectations, more and more applications require intensive computing resources and, most importantly, high consumption of data distributed throughout the environment. For this reason, there has been an increasing number of research efforts to cooperatively use geographically distributed resources, working in parallel and sharing resources and data. In fact, an application can be structured into a set of tasks organized through interdependent relationships, some of which can be effectively executed in parallel, notably speeding up the execution time. In this work a model is proposed aimed at offloading tasks execution in heterogeneous environments, considering different nodes computing capacity connected through distinct network bandwidths, and located at different distances. In the envisioned model, the focus is on the overhead produced when accessing remote data sources as well as the data transfer cost generated between tasks at run-time. The novelty of this approach is that the mechanism proposed for tasks allocation is data-flow aware, considering the geographical location of both, computing nodes and data sources, ending up in an optimal solution to a highly complex problem. Two optimization strategies are proposed, the Optimal Matching Model and the Staged Optimization Model, as two different approaches to obtain a solution to the task scheduling problem. In the optimal model approach a global solution for all application's tasks is considered, finding an optimal solution. Differently, the staged model approach is designed to obtain a local optimal solution by stages. In both cases, a mixed integer linear programming model has been designed intended to minimizing the application execution time. In the studies carried out to evaluate this proposal, the staged model provides the optimal solution in 76% of the simulated scenarios, while it also dramatically reduces the solving time with respect to optimal. Both models have pros and cons and, in fact, can be used together to complement each other. The optimal model finds the global optimal solution at high running time cost, which makes this model unpractical on some scenarios. The staged model instead, is faster enough to be used on those scenarios; however, the given solution might not be optimal in some cases.
机译：由于技术的进步和高苛刻的用户期望，越来越多的应用需要密集的计算资源，最重要的是，在整个环境中分布的数据的高消耗。因此，越来越多的研究努力来协同使用地理上分布式资源，并行使用并行和共享资源和数据。实际上，应用程序可以构造成通过相互依存关系组织的一组任务，其中一些可以并行地有效地执行，显着加速执行时间。在这项工作中，考虑通过不同网络带宽连接的不同节点计算能力，并位于不同的距离。在设想的模型中，焦点在访问远程数据源时产生的开销以及在运行时在任务之间生成的数据传输成本。这种方法的新颖性是提出用于任务分配的机制是数据流意识，考虑到计算节点和数据源的地理位置，以最佳的解决方案结束到高度复杂的问题。提出了两种优化策略，最佳匹配模型和分阶段优化模型，作为两种不同的方法，以获得任务调度问题的解决方案。在最佳模型方法中，考虑所有应用程序任务的全局解决方案，找到最佳解决方案。不同地，阶段模型方法旨在通过阶段获得局部最佳解决方案。在这两种情况下，设计了混合整数线性编程模型，用于最小化应用程序执行时间。在进行评估的研究中，分期模型在76％的模拟场景中提供了最佳解决方案，同时它还显着降低了最佳的求解时间。两种型号都有利弊，实际上可以一起使用以相互补充。最佳模型以高运行时间成本找到全局最佳解决方案，这使得这种模型在某些情况下不实用。阶段模型更快地用于这些场景;但是，在某些情况下给定的解决方案可能不是最佳的。

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