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Network-constrained packing of brokered workloads in virtualized environments

机译:在虚拟环境中受网络约束的代理工作负载的打包

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摘要

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.
机译:在云平台中,尤其是对于数据密集型应用程序,为资源分配提供性能可预测性保证变得越来越重要,在云计算平台中,性能很大程度上取决于在分配给应用程序的虚拟化资源基础上的各种计算/存储主机之间的可用数据传输速率。现有的资源分配解决方案要么假设应用程序管理其虚拟化资源之间的数据传输,要么假定云提供商管理其内部网络资源。随着云平台中经纪服务的普及,对资源分配解决方案的需求提供了可预测性保证在设置中,代理既不能管理/控制应用程序调度,也不能管理云提供者资源。本文解决了这个问题,因为我们定义了网络约束打包(NCP)问题,以发现代理资源到具有性能可预测性的应用程序的最佳映射。我们证明NCP是NP难的,并且我们定义了两个特殊的问题实例,可以有效地找到确切的解决方案。我们开发了一种贪婪的启发式方法来解决NCP问题的一般情况,并使用各种应用程序工作负载和网络模型的仿真来评估其效率。

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