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

软件系统正变得越来越无处不在，对我们的生活功能也越来越重要。一个日益重要的需求是，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，系统开发人员今天合并了手写的运行时适应策略，以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，以及技术变化的速度不断增加，手工方法无法跟上。相反，我们必须将适应战略的演变视为首要问题。本研究开发了新的机制来自动适应和发展适应策略本身。我们的高级方法是重用以前的领域或专家知识，为灵活战略的构建提供信息，能够适应意想不到的变化以及系统或环境变化的各种潜在维度。未来一代的软件系统将需要针对多种交互、难以测量和不断发展的质量、属性和优先级自动优化。现有的工作为构建复杂的软件系统提供了方法，这些系统可以适应特定环境的变化，例如不断变化的环境条件、基础设施的可用性或用户需求，同时继续以所需的质量水平提供服务。我们的启发是随机搜索方法对于自适应软件系统特别有前途，特别是对于处理自适应策略的演变，最近的工作部分地证明了这一点，即将此类技术扩展到复杂的源代码级软件问题。本研究利用随机搜索为自适应领域的适应策略进化奠定了原则基础。由此产生的技术家族重用、重组，以及以先前关于给定系统的知识为基础，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)在适应情景中可采用的策略，包括在这些策略之间进行选择的机制，以及关于这些策略的适用性、成本、效果、成功可能性等信息；(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。每一种策略的统一之处在于以前的领域或专家知识的存在，这些知识可以用于不断发展的适应性策略。

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.