Handling heterogeneity and unpredictability are two core problems in pervasive computing. The challenge is to seamlessly integrate devices with varying computational resources in a dynamic environment to form a cohesive system that can fulfill the needs of all participants. Existing work on systems that adapt to changing requirements typically focuses on optimizing individual variables or low-level Service Level Objectives (SLOs), such as constraining the usage of specific resources. While low-level control mechanisms permit fine-grained control over a system, they introduce considerable complexity, particularly in dynamic environments. To this end, we propose drawing from Active Inference (AIF), a neuroscientific framework for designing adaptive agents. Specifically, we introduce a conceptual agent for heterogeneous pervasive systems that permits setting global systems constraints as high-level SLOs. Instead of manually setting low-level SLOs, the system finds an equilibrium that can adapt to environmental changes. We demonstrate the viability of AIF agents with an extensive experiment design, using heterogeneous and lifelong federated learning as an application scenario. We conduct our experiments on a physical testbed of devices with different resource types and vendor specifications. The results provide convincing evidence that an AIF agent can adapt a system to environmental changes. In particular, the AIF agent can balance competing SLOs in resource heterogeneous environments to ensure up to 98% fulfillment rate.

翻译：处理异构性与不可预测性是普适计算中的两个核心问题。挑战在于如何将具有不同计算资源的设备无缝集成到动态环境中，形成一个能够满足所有参与者需求的协同系统。现有关于适应变化需求的系统研究通常侧重于优化单个变量或低层服务级别目标（SLO），例如限制特定资源的使用。虽然低层控制机制允许对系统进行细粒度控制，但它们引入了相当大的复杂性，尤其是在动态环境中。为此，我们提出借鉴主动推理（AIF）——一种用于设计自适应智能体的神经科学框架。具体而言，我们为异构普适系统引入了一种概念性智能体，允许将全局系统约束设定为高层SLO。系统无需手动设置低层SLO，即可找到能够适应环境变化的平衡状态。我们以异构与终身联邦学习作为应用场景，通过广泛的实验设计论证了AIF智能体的可行性。实验在由不同资源类型和供应商规格设备组成的物理测试平台上进行。结果提供了令人信服的证据，表明AIF智能体能够使系统适应环境变化。特别地，AIF智能体可在资源异构环境中平衡相互竞争的SLO，确保高达98%的满足率。