Design and evolution of highly adaptive software systems: Context, control and run-time validation

随着网络服务和社会技术生态系统的快速增长，这些现代、去中心化、分布式计算系统的复杂性给企业带来了重大挑战。从以商品为中心到以服务为中心的业务的持续发展需要新的创新方法来构建、运行、管理和发展软件系统。最终用户越来越多地要求企业提供灵活、弹性、基于位置、面向服务、分散、节能、自我修复和自我优化的智能软件系统。具有这种高度自适应特性的系统必须能够在运行时以完全或半自动自适应的形式调整其行为，以响应其对环境和自身状态的感知。 在这项研究中，我们的目标是研究创新的控制和上下文驱动的自适应，以产生更智能的应用程序。实现这些特性的最有前途的方法之一是为软件系统配备反馈控制，以解决固有系统动态的管理。应用范围从智能Web服务和位置商业智能到自适应云调度和系统诊断。我们编排运行时系统自适应的创新方法基于动态上下文和控制理论，并涉及运行时验证和确认。我们将通过与行业广泛合作，用真实世界的系统验证我们的结果。在这个过程中，我们将培养许多研究生以及工业合作者。 这项研究是及时的，对我们国家具有至关重要的战略意义。计算系统中更高的灵活性将导致更简单的系统，更好的弹性，更稳定，更高的可用性，更少的错误和更高的安全性。研究高度自适应的系统如何随着时间的推移而发展，并开发一个评估控制架构的框架，不仅可以减少长期自我管理系统的困难，还可以为如何为现有应用程序配备高度自适应的组件提供指导。

With the rapid growth of web services and socio-technical ecosystems, the complexity of these modern, decentralized, distributed computing systems presents significant challenges for businesses. The continuous evolution from goods-centric to service-centric businesses requires new and innovative approaches for building, running, managing, and evolving software systems. End-users increasingly demand that businesses provide smart software systems that are flexible, resilient, location-based, service-oriented, decentralized, energy-efficient, self-healing, and self-optimizing. A system with such highly adaptive properties must be capable of adjusting its behaviour at run-time in response to its perception of its environment and its own state in the form of fully or semi-automatic self-adaptation. In this research we aim to investigate innovative control and context driven self-adaptation to produce smarter applications. One of the most promising approaches to achieving such properties is to equip software systems with feedback control to address the management of inherent system dynamics. Applications range from smart web services and location business intelligence to adaptive cloud scheduling and system diagnosis. Our innovative approach to orchestrate run-time system adaptations is based on dynamic context and control theory and involves run-time verification and validation. We will validate our results with real-world systems by collaborating extensively with industry. In this process, we will train many graduate students as well as industrial collaborators. This research is timely and of critical strategic importance for our nation. More autonomicity in computing systems will lead to simpler systems, better resiliency, more stability, higher availability, fewer errors, and higher security. Investigating how highly adaptive systems evolve over time and developing a framework for evaluating control architectures will not only reduce the difficulties with long-lived self-managed systems, but also provide guidance on how to equip existing applications with highly adaptive components.