When multiple self-adaptive systems share the same environment and have common goals, they may coordinate their adaptations at runtime to avoid conflicts and to satisfy their goals. There are two approaches to coordination. (1) Logically centralized, where a supervisor has complete control over the individual self-adaptive systems. Such approach is infeasible when the systems have different owners or administrative domains. (2) Logically decentralized, where coordination is achieved through direct interactions. Because the individual systems have control over the information they share, decentralized coordination accommodates multiple administrative domains. However, existing techniques do not account simultaneously for both local concerns, e.g., preferences, and shared concerns, e.g., conflicts, which may lead to goals not being achieved as expected. Our idea to address this shortcoming is to express both types of concerns within the same constraint optimization problem. We propose CoADAPT, a decentralized coordination technique introducing two types of constraints: preference constraints, expressing local concerns, and consistency constraints, expressing shared concerns. At runtime, the problem is solved in a decentralized way using distributed constraint optimization algorithms implemented by each self-adaptive system. As a first step in realizing CoADAPT, we focus in this work on the coordination of adaptation planning strategies, traditionally addressed only with centralized techniques. We show the feasibility of CoADAPT in an exemplar from cloud computing and analyze experimentally its scalability.

翻译：当多个自适应性系统共享同一环境并具有共同目标时，它们需要在运行时协调各自的适应性行为，以避免冲突并实现目标。协调方式分为两种：（1）逻辑集中式协调，即由监督者完全控制各个自适应性系统。当系统分属不同所有者或管理域时，此类方法不可行。（2）逻辑去中心化协调，即通过直接交互实现协调。由于各系统对其共享信息具有控制权，去中心化协调能够兼容多个管理域。然而，现有技术无法同时兼顾局部关注点（例如偏好）与全局关注点（例如冲突），可能导致目标未能按预期实现。为弥补这一缺陷，我们提出将两类关注点统一表述为约束优化问题。本文提出的CoADAPT是一种去中心化协调技术，引入两类约束：表达局部关注点的偏好约束，以及表达全局关注点的一致性约束。在运行时，各自适应性系统通过分布式约束优化算法以去中心化方式求解该问题。作为CoADAPT的初步实现，本文重点研究传统上仅采用集中式技术的适应性规划策略协调问题。我们通过云计算领域的示例验证了CoADAPT的可行性，并实验分析了其可扩展性。