Machine Learning (ML) is a common tool to interpret and predict the behavior
of distributed computing systems, e.g., to optimize the task distribution
between devices. As more and more data is created by Internet of Things (IoT)
devices, data processing and ML training are carried out by edge devices in
close proximity. To ensure Quality of Service (QoS) throughout these
operations, systems are supervised and dynamically adapted with the help of ML.
However, as long as ML models are not retrained, they fail to capture gradual
shifts in the variable distribution, leading to an inaccurate view of the
system state. Moreover, as the prediction accuracy decreases, the reporting
device should actively resolve uncertainties to improve the model's precision.
Such a level of self-determination could be provided by Active Inference (ACI)
-- a concept from neuroscience that describes how the brain constantly predicts
and evaluates sensory information to decrease long-term surprise. We
encompassed these concepts in a single action-perception cycle, which we
implemented for distributed agents in a smart manufacturing use case. As a
result, we showed how our ACI agent was able to quickly and traceably solve an
optimization problem while fulfilling QoS requirements.

机器学习在分布式计算系统中被广泛应用于解释和预测行为，特别是在物联网设备产生大量数据时，通过边缘设备进行数据处理和机器学习训练。为了保证服务质量，系统通过机器学习进行监督和动态调整。然而，如果机器学习模型长时间不进行重新训练，它们可能无法准确捕捉到变量分布的渐变变化，导致对系统状态的错误预测。此外，随着预测准确度的降低，报告设备应主动解决不确定性以提高模型的精确性。脑神经科学中的主动推理（Active Inference）概念可以提供这种自主决策的能力，它描述了大脑如何不断预测和评估感觉信息以减少长期的惊奇感。我们在一个智能制造案例中实现了这些概念，通过一个单一的行动 - 感知循环实现了分布式智能体，展示了我们的主动推理代理能够在满足服务质量要求的同时快速和可追溯地解决优化问题。

边缘上的主动推理：一个设计研究

Active Inference on the Edge: A Design Study

Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres
are large scale, complex, heterogeneous, and distributed across multiple
networks and geographical boundaries. On the other hand, the Internet of Things
(IoT)-driven applications are producing a huge amount of data that requires
real-time processing and fast response. Managing these resources efficiently to
provide reliable services to end-users or applications is a challenging task.
The existing Resource Management Systems (RMS) rely on either static or
heuristic solutions inadequate for such composite and dynamic systems. The
advent of Artificial Intelligence (AI) due to data availability and processing
capabilities manifested into possibilities of exploring data-driven solutions
in RMS tasks that are adaptive, accurate, and efficient. In this regard, this
paper aims to draw the motivations and necessities for data-driven solutions in
resource management. It identifies the challenges associated with it and
outlines the potential future research directions detailing where and how to
apply the data-driven techniques in the different RMS tasks. Finally, it
provides a conceptual data-driven RMS model for DCS and presents the two
real-time use cases (GPU frequency scaling and data centre resource management
from Google Cloud and Microsoft Azure) demonstrating AI-centric approaches'
feasibility.

本文探讨了数据驱动资源管理系统在分布式计算系统中的应用及其挑战所在，并提供了一个概念性的数据驱动 RMS 模型，并以 GPU 频率调节和互联网数据中心资源管理两个实时案例证明该方法是可行的。