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

软件系统正变得越来越无处不在，对我们生活的运转也变得至关重要。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。为了解决这一问题，今天的系统开发人员结合了手写的运行时适配策略，以自动保持系统有效地运行。然而，随着软件系统在复杂性和普及性方面的增长，以及随着技术变化的速度不断增加，手动方法无法跟上。相反，我们必须把适应战略的演变作为首要关切。这项研究开发了自动适应和进化适应策略本身的新机制。我们的高层方法是重用以前的领域或专家知识来为灵活策略的构建提供信息，能够适应意外的变化和系统或环境变化的各种潜在维度。未来一代软件系统将需要针对多个交互的、难以测量的和不断变化的质量、属性和优先级进行自动优化。现有的工作提供了构建复杂软件系统的方法，这些方法能够适应某些情况的变化，例如变化的环境条件、基础设施可用性或用户需求，同时继续以所需的质量水平提供服务。我们鼓舞人心的见解是，随机搜索方法对于自适应软件系统特别有希望，特别是在处理自适应策略的演变方面，最近的工作将这种技术扩展到复杂的源代码级软件问题，这在一定程度上证明了这一点。本研究利用随机搜索为自适应领域中适应策略的进化奠定了原则性基础。所产生的技术系列重用、重组和以其他方式建立在关于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)系统的体系结构和部署；(2)可以在适应场景中部署的策略，包括在它们之间进行选择的机制以及关于它们的适用性、成本、效果、成功可能性等的信息；(3)系统的质量目标及其相对优先级；以及(4)控制系统部署环境的环境假设。这些战略中的统一因素是先前领域或专家知识的存在，可以利用这些领域或专家知识来发展适应战略。

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.