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

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。受到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重要，因为政府机构、企业和个人预计将越来越依赖这些服务。许多IT专业人士认为，缺乏保证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设置的服务可用性，并设计有效的资源供应和分配算法，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准确预测和云服务可用性/可靠性的成本效益保证的知识，显著推进最先进的技术。此项目的输出可用于：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供应的成本；2)在启用新的（关键任务）应用程序和服务时，减少由于服务中断而导致的收入和生产力方面的巨大损失。现有的确保可用性的方法是定性的，因为它们使用启发式方法来复制数据或限制应该放置在同一机架/服务器中的虚拟机（vm）的数量，以提高云服务的可靠性/可用性。然而，能够量化给定设置的可用性是至关重要的。通过分析（与测量或定性评估相反）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修复/备份模型的稳态概率分析。该项目采用全面的方法，通过严格的分析和广泛的实验来解决开放的挑战。更具体地说，该项目利用了两个大型HPC/云生产系统在PI？生成一组关于物理组件故障的丰富的细粒度数据（这在公共领域是不可用的）。然后对数据进行分析，以构建和验证/验证故障模型。基于故障模型，对于给定的n个虚拟机（vm）的基础设施即服务（IaaS）请求，服务持续时间为t个时间单位，期望的可用性级别为1，该项目开发了一个分析模型，以预测在服务持续时间(t)内，如果分配额外的k个备份虚拟机，可以实现的可用性。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供应和分配算法，以确定k的适当值（以及这n+k个vm的位置），以达到所需的可用性级别a。

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.