Current innovative distributed architectures, proposing on‐line services, involve more and more computing resources. From a provider point of view, the platform management leads to challenging problematic relating to resource allocation, which involve different kind of quality of service parameters, the provider has to focus on to keep his platform reliable and efficient. MFHS is a modular generic framework, which can be adapted to any distributed computing environment. Structured in modules, MFHS allows to discover the existing computing resources in terms of computing performance, network throughput and disk I/O speeds (Resources Discovery module) and to predict how the experiment should behave (Pi value). As the setting up of real experiments is often complex, MFHS allows: to make theoretical experimentation (based on models), to use any kind of distributed emulators, or to deploy experiments on real‐experimental platforms. In this article, these three environments are used to highlight the reliability of MFHS (measured Pi=90% against 94% for the predicted Pi). Deployment and scheduling studies have also been achieved using an experimental Cloud based on OpenStack while Emulab test‐bed has been used as emulator. During experiments, four QoS parameters are taken into account (Resources Monitoring module): energy consumption, cost, resource utilization, and makespan. These studies also includes a new heuristic called MMin, based on Max‐Min and Min‐Min algorithms. Experimentation section, proposes a detailed comparative analysis of these algorithms in terms of QoS results, while the abilities of the proposed heuristic MMin regarding the makespan metric is shown.

中文翻译：

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MFHS：异构系统的模块化调度框架

当前创新的分布式架构，提出在线服务，涉及越来越多的计算资源。从供应商的角度来看，平台管理导致与资源分配相关的具有挑战性的问题，涉及不同类型的服务质量参数，供应商必须专注于保持其平台的可靠和高效。MFHS 是一个模块化的通用框架，可以适应任何分布式计算环境。MFHS 采用模块结构，允许在计算性能、网络吞吐量和磁盘 I/O 速度（资源发现模块）方面发现现有计算资源，并预测实验应如何运行（Pi 值）。由于实际实验的设置通常很复杂，MFHS 允许： 进行理论实验（基于模型），使用任何类型的分布式模拟器，或在真实的实验平台上部署实验。在本文中，这三种环境用于强调 MFHS 的可靠性（测量的 Pi=90%，而预测的 Pi 为 94%）。还使用基于 OpenStack 的实验性云实现了部署和调度研究，而 Emulab 测试台已被用作模拟器。在实验过程中，考虑了四个 QoS 参数（资源监控模块）：能耗、成本、资源利用率和完工时间。这些研究还包括一种名为 MMin 的新启发式算法，它基于 Max-Min 和 Min-Min 算法。实验部分在 QoS 结果方面对这些算法进行了详细的比较分析，同时显示了所提出的启发式 MMin 在完工时间度量方面的能力。