I-Corp: A Data-Driven Cloud Optimization Tool

I-Corps项目的更广泛影响/商业潜力是使用机器学习算法和优化/推理开发云管理软件，以适应不同的应用环境/需求，实现高级预测和分析，并自动生成可操作的建议。随着越来越多的企业和组织继续采用以云为中心的信息技术（IT）基础设施，对帮助优化其云运营的解决方案的需求越来越大，其目标是通过高级警报提高基础设施的成本和运营效率，最大限度地提高资源利用率和事件报告/预测，并确保安全合规性，例如访问控制和异常行为检测。虽然市场上有一些可用的云管理工具，但它们通常缺乏必要的智能来适应不同的应用程序环境/需求，支持高级预测和分析，或者动态地自动生成可操作的建议。自动化和智能的云管理工具将满足这些客户的需求。该工具将使中小型企业能够以可靠和安全的方式提供基于云的服务。这个I-Corps项目将探索可靠性即服务（RaaS）技术新发展的转化。RaaS是解决安全性和可靠性的云管理软件技术。为了实现完全自动化、无监督的RaaS操作，目标是利用多个循环神经网络（rnn）从丰富的云统计数据中挖掘和分解各种趋势（例如，业务增长和需求峰值）。这种挖掘和还原过程将允许拟议的人工智能（AI）推理引擎识别复杂的潜在趋势，并以协同方式对其进行分析，以获得最准确的分析。AI推理引擎在云管理中提供了基础设施监控、成本控制、效率优化和安全合规四大功能。此外，初步结果表明，基础设施监视器可以预测云异常，准确率超过91%。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact/commercial potential of this I-Corps project is the development of cloud management software using machine learning algorithms and optimization/reasoning to adapt to different application environments/demands, enable advanced forecasting and analytics, and automatically-generated, actionable recommendations. As more businesses and organizations continue to adopt a cloud-centric Information Technology (IT) infrastructure, there is an increasing demand for solutions to help optimize their cloud operations with the goal of improving the cost and operation efficiency of the infrastructure with advanced alerts, maximizing resource utilization and incident reporting/forecasting, and ensuring security compliance, e.g., access control and abnormal behavior detection. While there are some cloud management tools available on the market, they often lack the required intelligence to adapt to different application environments/demands, to enable advanced forecasting and analytics, or to automatically generate actionable recommendations on the fly. An automated and intelligent cloud management tool will address the needs of these customers. The tool will enable small- and medium-sized businesses to offer cloud-based services in a reliable and secure fashion.This I-Corps project will explore the translation of new developments of Reliability-as-a-Service (RaaS) technologies. RaaS are cloud management software technologies that address security and reliability. To enable fully automated, unsupervised operation of RaaS, the goal is to leverage multiple Recurrent Neural Networks (RNNs) to mine and decompose various trends (e.g., business growth and demand peaks) from rich cloud statistics. This mining and reduction process will allow the proposed artificial intelligence (AI) reasoning engine to identify complex underlying trends and analyze them in a synergistic fashion to obtain the most accurate analytics. The AI reasoning engine provides four functionalities in cloud management – Infrastructure Monitor, Cost Control, Efficiency Optimization, and Security Compliance. In addition, preliminary results show that the Infrastructure Monitor may predict cloud anomalies with an accuracy of over 91%.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.