CNS Core: Small: Principled Methodologies and Systems Support for Automated Cost-Effective Service Blending in the Emerging Public Cloud

越来越多的个人和组织依赖公共云提供商来满足其信息技术（IT）需求。这些云计算用户中有许多人都受到限制，因此，他们对降低云计算费用同时满足应用程序性能需求的方法感兴趣。云提供商提供了无数的服务类型（跨越基础设施、平台和软件即服务，以及每种服务的多样性），与往往限于少数服务类型的流行技术相比，混合这些服务可以为用户节省大量成本。然而，获得这样的混合权利是不平凡的，本身可能会造成重大的努力和成本。该项目旨在帮助用户克服这些障碍，显著自动化的过程中，具有成本效益的混合和规模的云服务。特别是，这种自动化将通过称为CoCo的云成本优化编译器实现。 应用程序代码注释的框架将允许用户根据他们的领域专业知识传达与混合相关的提示。CoCo将需要从根本上新颖的优化技术和算法来将用户代码转换为云就绪的形式，这将具有成本效益，同时满足性能要求。最后，还将开发一个运行时系统，用于不断适应动态工作量变化。所有这些想法都将在最先进的公共云平台上原型化并开源。该项目有可能大大简化将用户应用程序迁移到公共云的任务，并节省相应的成本。也许更重要的是，经过转换的代码预计会产生更低的经常性云账单，这是因为云服务类型的仔细混合和调整能够适应动态条件。这些创新可能对中小型用户特别有用，因为云迁移可能会带来重大的技术和成本障碍。该项目的教育和推广部分将使人们意识到服务混合所带来的成本节约，并与我们的开源原型相结合，将有助于激励云计算研究社区内相关主题的进一步创新。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A growing number of individuals and organizations rely on public cloud providers for their information technology (IT) needs. Many of these cloud users are budget-constrained and, therefore, interested in ways to reduce their cloud bills while still meeting their applications' performance needs. Cloud providers offer myriad service types (spanning infrastructure-, platform-, and software-as-a-service and diversity within each of these) and blending these can offer significant cost savings to users over prevalent techniques that tend to be limited to a small number of service types. However, getting such blending right is non-trivial and may itself pose significant effort and cost. This project aims to help users overcome such hurdles by significantly automating the process of cost-effectively blending and sizing cloud services. In particular, this automation will be realized via a cloud cost optimizing compiler called CoCo. A framework for application code annotation will allow users to convey blending-related hints based on their domain expertise. CoCo will require fundamentally novel optimization techniques and heuristics to transform user code into its cloud-ready form which will be cost-effective while meeting performance requirements. Finally, a runtime system for continual adaptation to dynamic workload changes will also be developed. All of these ideas will be prototyped on state of the art public cloud platforms and open-sourced. This project has the potential to significantly simplify the task of migrating user applications to the public cloud with attendant cost savings. Perhaps more importantly, the transformed code is expected to incur lower recurring cloud bills owing to careful blending and sizing of cloud service types that adapts to dynamic conditions. These innovations are likely to be especially useful to small/medium-sized users for whom cloud migration can pose significant technical and cost hurdles. The educational and outreach components of the project will create awareness of such cost savings offered by service blending and, in combination with our open-source prototypes, will help spur further innovations on related themes within the cloud computing research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.