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 disaggregated 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%.

The growing amount of data generated at the edge of the network, e.g., by Internet of Things (IoT) devices, made it indispensable to relocate computational power close to the data source. Meanwhile, data tends to accumulate in chunks and is frequently subject to resource-intensive transformations, such as privacy enforcement. These phenomena, which are summed up as “data gravity” and “data friction”, have an impact on data processing and the overall system. However, whereas cloud centers are able to dynamically adapt services, e.g., by provisioning additional resources, edge devices provide fewer options to react to changing workloads. To retain the option to process data locally, we present the idea of controlling data gravity and friction with Service Level Objectives (SLOs). We introduce Markov SLO Configurations (MSCs) as a novel approach to organizing performance metrics and elasticity strategies. MSCs, in conjunction with our presented architecture, enable the evaluation of SLOs, the context-based selection of elasticity strategy (i.e., corrective measures), and the execution of strategies directly on edge devices. Thus, we lay the foundation for a new generation of SLOs that can operate across multiple elasticity dimensions, e.g., by scaling quality of service (QoS).

The growing number of Internet of Things (IoT) devices generates massive amounts of diverse data, including personal or confidential information (i.e., sensory, images, etc.) that is not intended for public view. Traditionally, predefined privacy policies are usually enforced in resource-rich environments such as the cloud to protect sensitive information from being released. However, the massive amount of data streams, heterogeneous devices, and networks involved affects latency, and the possibility of having data intercepted grows as it travels away from the data source. Therefore, such data streams must be transformed on the IoT device or within available devices (i.e., edge devices) in its vicinity to ensure privacy. In this paper, we present a privacy-enforcing framework that transforms data streams on edge networks. We treat privacy close to the data source, using powerful edge devices to perform various operations to ensure privacy. Whenever an IoT device captures personal or confidential data, an edge gateway in the device’s vicinity analyzes and transforms data streams according to a predefined set of rules. How and when data is modified is defined precisely by a set of triggers and transformations - a privacy model - that directly represents a stakeholder’s privacy policies. Our work answered how to represent such privacy policies in a model and enforce transformations on the edge.