NeTS: Small: Demystifying the Role of Prediction Models: Bridging Prediction Algorithms and Resource Provisioning

软件部署必须仔细调配，以满足其性能要求，而不会浪费资源。如今，大多数资源供应解决方案都采用预测来估计未来的需求并相应地进行供应。然而，天真地使用预测可能会否定它的好处。例如，由于预测器中的不确定性，仅基于预测的平均值提供资源可能导致严重的性能违规。另一方面，虽然额外的资源供应可以消除性能违规，但它会大大增加资源浪费。该项目的目标是开发和利用误差模型，以充分发挥预测器的潜力。这个项目的主要智力贡献是通过研究预测误差模型来弥合预测器和资源提供解决方案之间的差距。这将通过三个主要方面来实现：㈠构建模型，捕捉预测误差的结构，包括随时间变化的相关性和质量; ㈡开发一个算法框架，纳入预测误差模型，并考虑转换成本和惩罚函数;以及（iii）设计系统以利用新的预测误差感知算法，包括多资源供应和资源放置解决方案。将使用可用的应用程序跟踪对解决方案进行实验评估。这项研究将使企业能够减少资源浪费，尽管预测误差很大。研究成果将通过与工业伙伴的技术转让机会传播。鉴于工作的性质，该项目将促进跨学科教育和研究的机会。特别是，该项目将直接有助于在计算机科学和应用数学，统计部门，这也将允许学生的联合咨询教授的跨学科课程。作为该项目的结果产生的所有数据，包括跟踪，软件，出版物和课件，将在项目存储库中公开提供：http://www.pace.cs.stonybrook.edu/prediction-models.html。这些数据将提供至少10年，如果需要，甚至更长时间。数据将由本地网络服务器维护，也将在外部公共互联网服务器上复制，例如github提供的服务器，这些服务器具有长期的耐用性和可靠性。

Software deployments must be carefully provisioned to meet their performance requirements without wasting resources. Most resource provisioning solutions today employ predictions to estimate future demand and provision accordingly. However, naively employing predictions can negate its benefits. For instance, provisioning resources based only on the predicted average can result in severe performance violations due to uncertainties in the predictor. On the other hand, while additionally resource provisioning can eliminate performance violations, it can substantially increase resource wastage. The goal of this project is to develop and leverage error models to fully realize the potential of predictors. The key intellectual contribution of this project is to bridge the gap between predictors and resource provisioning solutions by investigating the prediction error model. This will be accomplished via three main thrusts: (i) constructing models that capture the structure of prediction errors, including correlations and quality over time; (ii) developing an algorithmic framework to incorporate the prediction error models and account for switching costs and penalty functions; and (iii) designing systems to exploit the new prediction error-aware algorithms, including multi-resource provisioning and resource placement solutions. The solutions will be experimentally evaluated using available application traces. The research will allow businesses to reduce resource wastage despite significant prediction errors. The research results will be disseminated through technology transfer opportunities with industrial partners. Given the nature of the work, the project will advance interdisciplinary education and research opportunities. In particular, the project will directly contribute to interdisciplinary courses taught at the Computer Science and Applied Mathematics, and Statistics departments, which also will allow for joint advising of students. All data produced as a result of this project, including traces, software, publications, and courseware, will be made publicly available on the project repository: http://www.pace.cs.stonybrook.edu/prediction-models.html. The data will be made available for at least 10 years, and even longer if needed. Data will be maintained by local web servers and will also be replicated on external public Internet servers, such as those provided by github, which offer long-term durability and reliability.