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Optimizing virtual machine allocation for parallel scientific workflows in federated clouds

机译：优化联合云中并行科学工作流的虚拟机分配

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Cloud computing has established itself as an interesting computational model that provides a wide range of resources such as storage, databases and computing power for several types of users. Recently, the concept of cloud computing was extended with the concept of federated clouds where several resources from different cloud providers are inter-connected to perform a common action (e.g. execute a scientific workflow). Users can benefit from both single-provider and federated cloud environment to execute their scientific workflows since they can get the necessary amount of resources on demand. In several of these workflows, there is a demand for high performance and parallelism techniques since many activities are data and computing intensive and can execute for hours, days or even weeks. There are some Scientific Workflow Management Systems (SWfMS) that already provide parallelism capabilities for scientific workflows in single-provider cloud. Most of them rely on creating a virtual cluster to execute the workflow in parallel. However, they also rely on the user to estimate the amount of virtual machines to be allocated to create this virtual cluster. Most SWfMS use this initial virtual cluster configuration made by the user for the entire workflow execution. Dimensioning the virtual cluster to execute the workflow in parallel is then a top priority task since if the virtual cluster is under or over dimensioned it can impact on the workflow performance or increase (unnecessarily) financial costs. This dimensioning is far from trivial in a single-provider cloud and specially in federated clouds due to the huge number of virtual machine types to choose in each location and provider. In this article, we propose an approach named GraspCC-fed to produce the optimal (or near-optimal) estimation of the amount of virtual machines to allocate for each workflow. GraspCC-fed extends a previously proposed heuristic based on GRASP for executing standalone applications to consider scientific workflows executed in both single-provider and federated clouds. For the experiments, GraspCC-fed was coupled to an adapted version of SciCumulus workflow engine for federated clouds. This way, we believe that GraspCC-fed can be an important decision support tool for users and it can help determining an optimal configuration for the virtual cluster for parallel cloud-based scientific workflows.
机译：云计算已将自己确立为一种有趣的计算模型，可以为多种类型的用户提供广泛的资源，例如存储，数据库和计算能力。最近，云计算的概念已扩展为联邦云的概念，其中来自不同云提供商的多个资源相互连接以执行共同的动作（例如执行科学的工作流程）。用户可以从单一提供商的云环境和联合云环境中受益，以执行其科学的工作流程，因为他们可以按需获取必要数量的资源。在许多这样的工作流程中，由于许多活动需要大量的数据和计算，并且需要执行数小时，数天甚至数周的时间，因此需要高性能和并行技术。已经有一些科学工作流程管理系统（SWfMS）为单一提供商云中的科学工作流程提供了并行功能。它们中的大多数依赖于创建虚拟集群来并行执行工作流。但是，他们还依赖用户来估计要分配的虚拟机数量，以创建该虚拟集群。大多数SWfMS在整个工作流程执行过程中都使用用户进行的初始虚拟集群配置。确定虚拟群集的大小以并行执行工作流是当务之急，因为如果虚拟群集的大小不足或过大，则会影响工作流性能或增加（不必要）的财务成本。由于在每个位置和提供者中都有大量的虚拟机类型可供选择，因此在单一提供商的云中，尤其是在联邦云中，这种规模的配置绝非易事。在本文中，我们提出了一种名为GraspCC-fed的方法，以针对要分配给每个工作流的虚拟机数量产生最佳（或接近最佳）估计值。 GraspCC馈送扩展了先前提出的基于GRASP的启发式算法，用于执行独立应用程序，以考虑在单一提供商和联合云中执行的科学工作流。对于实验，将GraspCC馈入的SciCumulus工作流引擎的改进版本与联邦云耦合。这样，我们相信GraspCC可以为用户提供重要的决策支持工具，并且可以帮助确定基于并行云的科学工作流程的虚拟集群的最佳配置。

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