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: 1409809
• 负责人: Chunming Qiao
• 金额: $ 30万
• 依托单位: SUNY at Buffalo
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409809&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.

云计算将显著改变IT行业的格局，并在许多方面影响经济和社会。云服务的可靠性和可用性受到各种硬件和软件组件故障的影响，变得越来越重要，因为政府机构，企业和人们越来越依赖这些服务。许多IT专业人士认为，缺乏云服务和应用程序的高可用性是阻碍云服务成功实施的首要问题，其次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环境下的服务可用性，并设计有效的资源配置和分配算法，以保证云服务所需的高可用性水平。该项目预计将通过深入了解有关准确预测和云服务可用性/可靠性的成本效益保证的知识来显着推进最先进的技术。该项目的产出可用于：1）提高服务可用性、性能和资源利用率，同时最大限度地降低过度配置的成本，2）减少由于服务中断而造成的收入和生产力的巨大损失，同时实现新的（关键任务）应用程序和服务。现有的确保可用性的方法是定性的，因为它们使用数据库复制数据或限制虚拟机的数量（虚拟机），这些虚拟机应放置在同一机架/服务器中，以提高云服务的可靠性/可用性。但是，必须能够量化给定环境的可用性。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基于广泛的故障和维修/备份模型的瞬态而不是稳态概率分析。该项目通过严格的分析和广泛的实验，采用整体方法来解决开放的挑战。更具体地说，该项目利用两个大规模的HPC/云生产系统在PI？的机构来生成一组丰富的关于物理组件故障的细粒度数据（这些数据在公共领域中不可用）。然后分析数据以构建和验证/确认故障模型。基于故障模型，对于给定的n个虚拟机（VM）的IaaS请求，t个时间单位的服务持续时间和期望的可用性级别a1，该项目开发了一个分析模型来预测在服务持续时间（t）内可以实现的可用性，如果额外分配k个备份VM。该项目还开发了具有成本效益的、基于多目标优化的云资源配置和分配算法，这些算法可以确定k的适当值（以及这些n+k个虚拟机的位置），以实现所需的可用性级别a。