SHF: SMALL: Evolution of Self-adaptive Systems using Stochastic Search

SHF：SMALL：使用随机搜索的自适应系统的演化

• 批准号: 1618220
• 负责人: David Garlan
• 金额: $ 49.99万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618220&HistoricalAwards=false
• 关键词: SHF; SMALL; Evolution; Self; adaptive

Software systems are becoming more ubiquitous and critical to the functioning of our lives. An increasingly important requirement is to maintain high availability of these systems even in the face of changing requirements, faults, and resources. To address that concern, system developers today incorporate hand-written run-time adaptation strategies to automatically keep a system functioning effectively. However, as software systems grow in both complexity and ubiquity, and as the rate of technological change continues to increase, manual approaches cannot keep up. We must instead treat the evolution of adaptation strategies as a first-order concern. This research develops new mechanisms to automatically adapt and evolve the adaptation strategies themselves. Our high-level approach is to reuse previous domain or expert knowledge to inform the construction of flexible strategies, able to adapt to unanticipated changes and to various potential dimensions of system or environmental change.Future-generation software systems will need to automatically optimize for multiple interacting, difficult-to-measure, and evolving qualities, properties, and priorities. Existing work provides methods for constructing complex software systems that can adapt to the changing of certain circumstances such as changing environmental conditions, infrastructure availability, or user demands,while continuing to provide service at required quality levels. Our motivating insight is that stochastic search methods are especially promising forself-adaptive software systems, and in particular for tackling the evolution of self-adaptation strategies, as evidenced in part by recent work that scales such techniques to complex source-level software problems. This research develops a principled foundation for the evolution of adaptation strategies in the self-adaptive domain, using stochastic search. The resulting family of techniques reuses, recombines, and otherwise builds upon previous knowledge about a given system to adapt to four major potential change dimensions: (1) the system's architecture and deployment; (2) the tactics that can be deployed in an adaptation scenario, including mechanisms to choose between them and information regarding their applicability, costs, effects, success likelihood, etc.; (3) the system's quality goals, and their relative priorities; and (4) the environmental assumptions that control the context in which the system is deployed. The unifying factor in each of these strategies is the existence of previous domain or expert knowledge that can be leveraged for evolving adaptive strategies moving forward.

软件系统变得越来越无处不在，对我们生活的运作至关重要。一个越来越重要的要求是即使面对不断变化的需求，故障和资源，也要保持这些系统的高可用性。为了解决这个问题，系统开发人员今天结合了手写的运行时间适应策略，以自动保持系统有效运行。但是，随着软件系统的复杂性和普遍性的增长，随着技术变革的速度不断提高，手动方法无法跟上。相反，我们必须将适应策略的演变视为一阶关注。 这项研究开发了自动适应和发展适应策略本身的新机制。 我们的高级方法是重复使用以前的领域或专家知识，以告知灵活策略的构建，能够适应意外的变化以及系统或环境变化的各种潜在维度。Future-Future-Generation Software Systems将需要自动优化多个交互，难以进行衡量，限制以及不断发展的质量，属性，物业和优先级。 现有工作提供了构建复杂软件系统的方法，这些系统可以适应某些情况的改变，例如改变环境条件，基础架构可用性或用户需求，同时继续以所需的质量水平提供服务。 我们激励的见解是，随机搜索方法对于自适应软件系统特别有希望，尤其是针对自我适应策略的演变，这在某种程度上是通过将这种技术扩展到复杂源级软件问题的最新工作所证明的。这项研究为自适应领域的适应策略演变而成的原则基础是使用随机搜索。 由此产生的技术家族重新，重新组合和以其他方式建立在先前有关给定系统的知识的基础上，以适应四个主要的潜在变化维度：（1）系统的体系结构和部署； （2）可以在适应方案中部署的策略，包括可以在它们之间进行选择的机制以及有关其适用性，成本，效果，成功可能性等信息的机制； （3）系统的质量目标及其相对优先事项； （4）控制部署系统的环境假设。这些策略中每种策略中的统一因素是存在先前领域或专家知识的存在，这些领域或专家知识可以利用发展的适应性策略。