CSR:Medium:Collaborative Research: An Analytical Approach to Quantifying Availability (AQUA) for Cloud Resource Provisioning and Allocation

CSR：中：协作研究：量化云资源配置和分配的可用性 (AQUA) 的分析方法

• 批准号: 1409256
• 负责人: Gregor von Laszewski
• 金额: $ 10万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409256&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Analytical

Cloud computing will significantly transform the landscape of the IT industry and also impact the economy and society in many ways. The reliability and availability of cloud services, affected by various hardware and software component failures, becomes increasingly more critical, as government agencies, business and people are expected to rely more and more on these services. Lack of a guaranteed high availability of cloud services and applications is considered by many IT professionals as the top concern for preventing a successful implementation of cloud services, followed by device based security and cloud application performance. This project aims to predict the service availability for a given setting, and design effective resource provisioning and allocation algorithms to guarantee a high availability level required by cloud services. The project is expected to significantly advance the state-of-the-art by offering deep insights into the knowledge about accurate prediction and cost-effective guarantee of availability/reliability of cloud services. The outputs from this project can be used to 1) improve service availability, performance and resource utilization while minimizing the cost of overprovisioning, 2) reduce huge losses in revenue and productivity due to service outages while enabling new (mission-critical) applications and services.The existing approaches to ensuring availability are qualitative in that they use heuristics to duplicate data or restrict the number of virtual machines (VMs) that should be placed in the same rack/server to improve reliability/availability of cloud services. However, it is essential to be able to quantify availability for a given setting. Quantifying availability for an often finite service duration via analysis (as opposed to measurement or qualitative evaluation) requires transient, instead of steady state probability analysis based on a wide range of failure and repair/backup models. This project takes a holistic approach to addressing the open challenges via both rigorous analysis and extensive experiments. More specifically, the project leverages two large-scale HPC/Cloud production systems at PI?s institutions to generate a rich set of fine-grained data about physical component failures (which is not available in the public domain). The data is then analyzed to build and verify/validate failure models. Based on the failure models and for a given Infrastructure-as-a-Service (IaaS) request for n virtual machines (VMs), a service duration of t time units and a desired availability level a 1, the project develops an analytical model to predict the availability that can be achieved for the service duration (t), if an additional k backup VMs are allocated. The project also develops cost-effective, multi-objective optimization based cloud resource provisioning and allocation algorithms that determine the appropriate value for k (and the placement of these n+k VMs) in order to achieve the required availability level a.

云计算将显着改变IT行业的景观，并在许多方面影响经济和社会。受各种硬件和软件组件故障影响的云服务的可靠性和可用性变得越来越关键，因为政府机构，商业和人员应越来越依赖这些服务。许多IT专业人员认为缺乏可保证的云服务和应用程序的高可用性是防止成功实施云服务的最大关注点，然后是基于设备的安全性和云应用程序性能。该项目旨在预测给定设置的服务可用性，并设计有效的资源提供和分配算法，以确保云服务所需的高可用性水平。预计该项目将通过深入了解云服务的可用性/可靠性的准确预测和具有成本效益的保证，从而大大提高最新技术。该项目的产出可以用于1）提高服务的可用性，绩效和资源利用，同时最大程度地减少过度配置的成本，2）减少由于服务中断而造成的收入和生产率的巨大损失，同时启用新的（任务）应用程序和服务的新（任务）方法。提高云服务的可靠性/可用性。但是，必须能够量化给定设置的可用性。通过分析（而不是测量或定性评估）来量化通常有限的服务持续时间的可用性，需要瞬态，而不是基于广泛的故障和维修/备份模型的稳态概率分析。该项目采用一种整体方法来通过严格的分析和广泛的实验来应对开放挑战。更具体地说，该项目利用PI机构的两个大规模的HPC/云生产系统来生成有关物理组件故障的丰富细粒度数据（公共领域中不可用）。然后分析数据以构建和验证/验证故障模型。根据故障模型和给定的基础架构 - AS-A-Service（IAAS）对N虚拟机（VM）的请求，T时间单元的服务持续时间以及所需的可用性级别A 1，该项目开发了一个分析模型，以预测可用于服务持续时间（T）的可用性（如果是额外的K备份VM，则可以实现服务持续时间。该项目还开发了基于成本效益的多目标优化的云资源提供和分配算法，这些算法确定了K（以及这些N+K VM的放置）的适当值，以达到所需的可用性级别a。