The EXIGENCE project has released Deliverable D3.2, Final Report on Protocols and Mechanisms for Energy Usage Reduction, presenting the final outcomes of Work Package 3 on energy- and carbon-aware optimisation mechanisms for future 6G systems.
The deliverable focuses on how digital services can be designed, orchestrated and incentivised to reduce energy consumption and carbon footprint without compromising service quality. It presents three main families of mechanisms: energy- and carbon-aware orchestration, runtime service scheduling, and incentive mechanisms for energy- and carbon-aware behaviour.
A key part of the work concerns green orchestration mechanisms integrated with platforms such as Kubernetes and Open Source MANO. These mechanisms support sustainable workload placement, adaptive service operation and dynamic resource allocation based on energy consumption, carbon intensity and infrastructure efficiency indicators.
The report also describes extensions such as virtual Energy-Aware States and green scheduling plugins that allow services to adapt to more sustainable operating modes.
The deliverable further investigates Radio Access Network energy optimisation, where mechanisms such as dynamic cell shutdown, radio bandwidth optimisation, MIMO level adaptation and radio port power optimisation can deliver significant savings. Experimental work shows that coordinated RAN configuration can reduce energy consumption by up to 60–70% for baseband processing and up to 40–55% at system level in selected scenarios.
Figure 1 – Orchestration system high-level architecture
For distributed AI services, D3.2 presents runtime scheduling and placement mechanisms that exploit renewable energy availability, optimise service variants and reduce energy consumption during AI training and inference. Reported results include reductions by factors of 10 and 20 for specific AI training mechanisms, as well as zero- or near-zero-carbon operation for time-insensitive AI workloads.
The report also presents incentive mechanisms designed to encourage users and service providers to select greener service configurations. These include gamified incentives for video streaming, subscription-based and lottery-based mechanisms, and carbon-credit-inspired reward models. In video streaming scenarios, such mechanisms can support traffic and energy reductions by a factor of 3, while AI inference mechanisms can support a five-fold reduction in carbon footprint.
Figure 2 – Comparison of cumulative emissions using fixed and adaptive strategies
Figure 3: Total Carbon footprint saving relative to the Baseline (tCO2e/Year)
Overall, D3.2 concludes that EXIGENCE has achieved Project Objective 2 by demonstrating mechanisms that reduce energy consumption and carbon footprint across video streaming, AI training, AI inference and RAN-related use cases. The deliverable provides a concrete basis for implementing greener ICT services through the combined use of measurement, optimisation and incentivisation.
References
- EXIGENCE Deliverable D3.2 – Final Report on Protocols and Mechanisms for Energy Usage Reduction, April 2026.
- Relevant referenced deliverables: D1.5 – Final System Architecture, D2.2 – Energy consumption and production metering and data observability methods, D3.1 – Report on protocols and mechanisms for energy usage reduction.
Author
ScanLab
Mr. Panagiotis Kontopoulos obtained his four-year Bachelor of Science (B.Sc.) and two-year Master of Science (M.Sc.) from National and Kapodistrian University of Athens (UoA), in the Department of Informatics and Telecommunications. Currently works as a Research Associate in the Software Centric & Autonomic Networking (SCAN) Lab of UoA in conjunction with his Doctor of Philosophy (PhD) from National and Kapodistrian University of Athens (UoA), in the Department of Informatics and Telecommunications, with a specialisation in Artificial Intelligence, Telecommunications and Networks. His main fields of interest are Software-Defined Networking (SDN), Software-Defined Wireless Local Area Networking (SDWLAN), distributed systems, mobile communication systems and services in future networks.