Gui Zhipeng, Yang Chaowei, Xia Jizhe, Huang Qunying, Liu Kai, Li Zhenlong, Yu Manzhu, Sun Min, Zhou Nanyin, Jin Baoxuan
NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, Virginia, United States of America; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei Province, China.
NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, Virginia, United States of America.
PLoS One. 2014 Aug 29;9(8):e105297. doi: 10.1371/journal.pone.0105297. eCollection 2014.
Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).
云计算正在成为新一代的计算基础设施，许多云供应商提供不同类型的云服务。如何为特定应用选择最佳云服务是一项极具挑战性的任务。应对这一挑战需要在计算需求之外，平衡业务需求、技术、政策和偏好等多个因素。本文推荐了一种在基础设施即服务（IaaS）和平台即服务（PaaS）层面选择最佳公共云服务的机制。一个系统框架及相关工作流程包括云服务筛选、解决方案生成、评估以及公共云服务的选择。具体而言，我们提出以下内容：一个用于整合来自不同提供商的异构云信息的分层信息模型以及相应的云信息收集机制；一个用于对云服务进行分类和筛选的云服务分类模型以及一个用于提供创建特定应用配置解决方案规则的应用需求模式；以及一个偏好感知解决方案评估模式，用于根据应用提供商的偏好评估和推荐解决方案。为测试所提出的框架和方法，开发了一个云服务咨询工具原型，之后进行了相关实验。结果表明，所提出的系统实时收集/更新/记录来自多个主流公共云服务的云信息，根据用户规格和可接受的成本预测生成可行的云配置解决方案，从多个方面（如计算能力、潜在成本和服务级别协议，SLA）评估解决方案，并基于用户偏好和实际云供应提供合理建议；并通过交互式网络图形用户界面（GUI）直观地呈现和比较解决方案。
