Collective adaptive systems are new emerging computational systems consisting of a large number of interacting components and featuring complex behaviour. These systems are usually distributed, heterogeneous, decentralised and interdependent, and are operating in dynamic and possibly unpredictable environments. Finding ways to understand and design these systems and, most of all, to model the interactions of their components, is a difficult but important endeavour. In this article we propose a language-based approach for programming the interactions of collective-adaptive systems by relying on attribute-based communication; a paradigm that permits a group of partners to communicate by considering their run-time properties and capabilities. We introduce AbC, a foundational calculus for attribute-based communication and show how its linguistic primitives can be used to program a sophisticated variant of the well-known problem of Stable Allocation in Content Delivery Networks. In our variant, content providers are assigned to clients based on collaboration and by taking into account the preferences of both parties in a fully anonymous and distributed settings. We also illustrate the expressive power of attribute-based communication by showing the natural encoding of group-based, publish/subscribe-based and channel-based communication paradigms into AbC.

集体自适应系统是新兴的计算系统，由大量相互作用的组件组成，并且具有复杂的行为。这些系统通常是分布式的，异构的，分散的和相互依赖的，并且在动态且可能不可预测的环境中运行。寻找理解和设计这些系统，最重要的是为它们的组件之间的相互作用建模的方法是一项艰巨而重要的工作。在本文中，我们提出了一种基于语言的方法，用于通过依赖基于属性的通信来编程集体自适应系统的交互。一种允许一组合作伙伴通过考虑其运行时属性和功能进行通信的范例。我们介绍AbC，这是基于属性的通信的基础演算，并展示了如何使用其语言原语来编程内容交付网络中众所周知的稳定分配问题的复杂变体。在我们的变体中，内容提供者基于协作并在完全匿名和分布式设置中考虑了双方的偏好而分配给客户端。我们还通过将基于组，基于发布/订阅和基于通道的通信范例的自然编码显示为AbC来说明基于属性的通信的表达能力。