Computing Continuum (CC) systems are challenged to ensure the intricate
requirements of each computational tier. Given the system's scale, the Service
Level Objectives (SLOs) which are expressed as these requirements, must be
broken down into smaller parts that can be decentralized. We present our
framework for collaborative edge intelligence enabling individual edge devices
to (1) develop a causal understanding of how to enforce their SLOs, and (2)
transfer knowledge to speed up the onboarding of heterogeneous devices. Through
collaboration, they (3) increase the scope of SLO fulfillment. We implemented
the framework and evaluated a use case in which a CC system is responsible for
ensuring Quality of Service (QoS) and Quality of Experience (QoE) during video
streaming. Our results showed that edge devices required only ten training
rounds to ensure four SLOs; furthermore, the underlying causal structures were
also rationally explainable. The addition of new types of devices can be done a
posteriori, the framework allowed them to reuse existing models, even though
the device type had been unknown. Finally, rebalancing the load within a device
cluster allowed individual edge devices to recover their SLO compliance after a
network failure from 22% to 89%.

我们提出了一个协同边缘智能的框架，使各个边缘设备能够发展对如何执行其性能目标的因果理解，并将知识传递以加快异构设备的入门进程，从而增加性能目标的范围。通过合作，它们提高了性能目标的实现范围。在保证视频流媒体期间的服务质量和用户体验质量方面，我们实施了该框架并评估了一个使用案例。我们的结果显示，边缘设备仅需进行十轮训练即可确保四个性能目标；此外，底层因果结构也具有合理的可解释性。即使设备类型未知，也可以后续添加新类型的设备，该框架允许它们重复使用现有模型。最后，通过在设备集群内重新平衡负载，个别边缘设备能够将其在网络故障后的性能目标符合度从 22% 恢复到 89%。

计算连续体中的平衡通过主动推理

Equilibrium in the Computing Continuum through Active Inference

Compute Continuum (CC) systems comprise a vast number of devices distributed
over computational tiers. Evaluating business requirements, i.e., Service Level
Objectives (SLOs), requires collecting data from all those devices; if SLOs are
violated, devices must be reconfigured to ensure correct operation. If done
centrally, this dramatically increases the number of devices and variables that
must be considered, while creating an enormous communication overhead. To
address this, we (1) introduce a causality filter based on Markov blankets (MB)
that limits the number of variables that each device must track, (2) evaluate
SLOs decentralized on a device basis, and (3) infer optimal device
configuration for fulfilling SLOs. We evaluated our methodology by analyzing
video stream transformations and providing device configurations that ensure
the Quality of Service (QoS). The devices thus perceived their environment and
acted accordingly -- a form of decentralized intelligence.

利用基于 Markov blankets 的因果关系过滤器，基于设备的分散评估和推断最优设备配置来实现计算连续系统中的服务质量目标。