Introducing AdaptoFlow – Newly Funded EU Co-Funded Project

The AILab, under the University of Nicosia Research Foundation (UNRF) umbrella, has been awarded a newly funded Horizon project through the TrialsNet ( open call (Horizon-JU-SNS-2022).

The focal point of AdaptoFlow is to introduce a service supporting AI/ML inference and data stream processing for applications deployed in geo-distributed realms so that data movement, energy consumption, and app-level latency are reduced by adapting the intensity of the execution at runtime. Adaptation will consider the evolution of IoT-generated data and the state of the underlying computational resources of edge nodes, while maintaining acceptable QoS guarantees requested by application operators. In a nutshell, AdaptoFlow will support the UCs with data-driven intelligence in the form of:

(i) adaptive data stream processing, where AdaptoFlow will output runtime suggestions to the monitoring module of the UCs for what the monitoring intensity should be. This will be performed by monitoring the data stream behavior dynamics of the numerical data extracted by the wifi access points (e.g., variance, entropy), so that the intensity at which the data consumed to output crowd analytics (i.e., people counting) is dynamically adjusted. This will reduce the volume of data that must be processed and provide “breathing space” to the analytics modules during phases of low volatility in the data behavior dynamics;

(ii) energy-aware model swapping, where AdaptoFlow will aid Edge AI services in providing a suggestion so that in the presence of limited resource capacity, the AI/ML model in use can be swapped on-the-fly with another readily available, but less complex, model. With model swapping, AI services can achieve energy savings by employing less computational effort for runtime inference tasks and at the same time, user experience is not negatively impacted with latency bounded by using a less complex model. The key goal of the AdaptoFlow intelligent mechanisms is to optimize resource use and improve service responsiveness, while at the same time, bound response quality (e.g., classification accuracy) to acceptable user-desired levels.

Dr. Demetris Trihinas from the AILab will be assuming the role of the Scientific Coordinator for AdaptoFlow.

Stay tuned for more updates, including open-source code, demos and research papers!

Illustration of the AdaptoFlow workflow