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普遍，并且对我们的生活至关重要。一个日益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，当今的系统开发人员采用了手写的运行时适应策略来自动保持系统有效运行。然而，随着软件系统的复杂性和普遍性的增长，以及技术变革速度的不断加快，手动方法已经无法跟上。相反，我们必须将适应策略的演变视为首要问题。 这项研究开发了新的机制来自动适应和进化适应策略本身。 我们的高级方法是重用以前的领域或专家知识来构建灵活的策略，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。未来的软件系统将需要自动优化多个相互作用的、难以衡量的、不断变化的品质、属性和优先级。 现有的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环境的变化，例如不断变化的环境条件、基础设施可用性或用户需求，同时继续提供所需质量水平的服务。 我们的激励性见解是，随机搜索方法对于自适应软件系统特别有前景，特别是对于解决自适应策略的演变，最近将此类技术扩展到复杂的源代码级软件问题的工作部分证明了这一点。这项研究利用随机搜索为自适应领域的适应策略的演变奠定了原则基础。 由此产生的技术系列重用、重组或以其他方式构建在有关给定系统的先前知识的基础上，以适应四个主要的潜在变化维度：（1）系统的架构和部署； （2）在适应场景中可以部署的策略，包括它们之间的选择机制以及有关其适用性、成本、效果、成功可能性等的信息； (3) 体系的质量目标及其相对优先级； (4) 控制系统部署环境的环境假设。这些策略的统一因素是先前领域或专家知识的存在，可以利用这些知识来不断发展自适应策略。