The drive to establish a cloud-to-edge continuum, spanning multiple heterogeneous compute regions, is progressively eroding the boundaries of security perimeters, calling for a zero-trust approach to security, where nothing, not even inside an organisation’s network, can be implicitly trusted. In such a scenario, a resource- and energy-efficient, opportunistic monitoring of users, services, platform, and infrastructure becomes paramount to enhance management and security in cloud-to-edge environments.

Monitoring and auditing have received increasing attention from research and industry in the recent years. Crucially, novel technologies have emerged to programmatically gather information from network flows, system calls, and other sources. These technologies, including eBPF (extended Berkeley Packet Filter) and P4, fall under the umbrella of the so-called programmable data planes.

The objective of this PhD endeavour is to design, implement and evaluate novel monitoring solutions capable of opportunistically diving into the appropriate depth of data collection, defining the right mix of data and user/control plane functions and their location. Additionally, they should be programmatically tailored to serve security and data analysis applications, while being suitable for dynamic scaling and orchestration. The overarching goal is to contain their footprint while delivering the required information effectively.