Learning Clouds

学习云

• 批准号: RGPIN-2020-05819
• 负责人: Jacobsen, HansArno
• 金额: $ 3.5万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740848
• 关键词: Learning; Clouds

Cloud computing is witnessing an unprecedented popularity with many organizations turning to cloud-hosted solutions for their applications and even for operating entire computing infrastructures. Those organizations who shy away from this transition are starting to deploy private clouds to offer similar capabilities in-house, presupposing they would retain better control. The benefits of cloud computing are multifaceted: Cost reduction, avoiding redundancy, and global data sharing, to just name a few. Clouds are widely touted for elasticity, endless scalability, security, and cost reductions by cutting IT personnel and conserving resources. However, many of these promises are fallacies, as the proper resource management algorithms and control mechanisms are not well understood. Existing cloud management frameworks - and therefore the clouds they enable - require the cloud user and developer to specify resource usage needs, an art that is often more based on black magic than on sound principles. Worse yet, users fear the worst case scenario and do not want their services and applications to under-perform, so massive resource over-provisioning is the answer and the norm rather than the exception. It remains poorly understood how much compute, memory, disk, and I/O capabilities an application system really requires to function. Decisions are ad hoc, more driven by budget than by true needs. Also, application systems do not always experience the same level of utilization, so at times expensive resources remain idle This is where Learning Clouds sets in and what we are trying to remedy in our research. Learning Clouds treat the cloud with its hosted services and applications as an autonomous system that self-manages, optimally allocating its resources in an online fashion guided via machine learning, rebalancing scarce resources on demand. The net outcome will be ease of application deployment as users are freed from making hard resource allocation decisions, better application performance as applications are assigned resources dynamically on an as-needed basis, and cost reductions as physical resources are allocated optimally and remain better utilized. Learning Clouds are based on novel reactive and proactive control techniques that strive to make better decisions as more information becomes available by leverages online convex optimization and anytime algorithm concepts.

随着许多组织将云托管解决方案用于其应用程序，甚至用于操作整个计算基础设施，云计算正在得到前所未有的普及。那些回避这种转变的组织开始部署私有云，在内部提供类似的功能，假设他们将保留更好的控制权。云计算的好处是多方面的：降低成本、避免冗余和全球数据共享，仅举几例。云被广泛地吹捧为弹性、无限的可伸缩性、安全性以及通过减少IT人员和节省资源来降低成本。然而，这些承诺中的许多都是谬论，因为正确的资源管理算法和控制机制并没有得到很好的理解。现有的云管理框架——以及它们所支持的云——要求云用户和开发人员指定资源使用需求，这往往是一种基于魔法而非合理原则的艺术。更糟糕的是，用户担心最坏的情况，不希望他们的服务和应用程序表现不佳，因此大量的资源过度供应是解决方案和常态，而不是例外。对于应用程序系统真正需要多少计算、内存、磁盘和I/O能力，人们仍然知之甚少。决策是临时的，更多的是由预算而不是实际需求驱动的。此外，应用程序系统并不总是具有相同的利用率水平，因此有时昂贵的资源仍然处于空闲状态。这就是学习云的作用所在，也是我们在研究中试图补救的问题。学习云将云及其托管服务和应用程序视为一个自我管理的自治系统，通过机器学习以在线方式优化分配资源，按需重新平衡稀缺资源。最终的结果将是简化应用程序部署，因为用户不再需要做出艰难的资源分配决策；提高应用程序性能，因为应用程序可以根据需要动态分配资源；降低成本，因为物理资源得到了最佳分配并得到了更好的利用。学习云基于新颖的反应式和主动控制技术，通过利用在线凸优化和随时算法概念，随着可用信息的增加，这些技术努力做出更好的决策。