# Future Sustainable Internet Energy-Defined Networking
## 4 Future Internet Energy-Defined Networking--Design Methods
### Methods for Network Energy Management and Associated Research Challenges
#### 4.4.5 Architectural-Level Energy Design Methods  
Architectural-level support for energy instrumentation functions (~10% of energy consumption); the methods' main goal: architectural integration. The specific critical methods include the following:  
1. The main aim is to deliver a flexible monitoring framework with programmable probes [5] deployed to enable the collection of energy consumption data from network (virtual) functions, devices, and slices that remain an issue; as such, this involvesdesigning precision telemetry, including probing agents that collect energy data from elements and components distributed and used by a cloud/edge-level energy analyzer. Such probes will be deployed near the corresponding devices, providing real-time information about energy consumption. 2. Precision network telemetry will provide data for energy self-management loops that will drive energy-related decisions affecting the devices' energy state. One such result is the ETSI's interface between network control and devices [23; 29], providing access to the energy management capabilities of future energy-aware telecommunication fixed-network nodes. Architectural-level network configuration functions (~10% of energy consumption); the methods' main goal: achieving optimal network reconfiguration. The specific critical methods include the following: 1. Energy network optimization could be performed periodically through closed-loop control. An approach involving closed control loops in three dimensions--connectivity, computing, and services--is needed to achieve this goal. 2. Novel energy consumption models: The development of power consumption models for connectivity and computational resources will assist in predicting and optimizing consumption for various service offerings. These models will guide network and service management decisions to derive greener configurations for the infrastructure and services. 3. Novel adaptive energy-aware resource management focusing on adaptive resource management approaches that execute at run-time to track connectivity and computation demand and regulate its distribution intelligently, allowing for more efficient resource utilization and energy conservation. 4. Semantic-aware real-time green routing: Alternative schemes for forwarding decision-making that circumvent expensive table lookups by employing a semantic networking paradigm, significantly increasing data-plane scalability and real-time network management. 5. Continual deterministic orchestration: The development of mechanisms by which connectivity and computational resources can be optimized jointly. These mechanisms will be realized through efficient Artificial Intelligence/Machine Learning (AI/ML) mechanisms based on a continual learning paradigm. At the same time, a crucial characteristic will be their distributed nature for scalability purposes. Such schemes will allow for richer policies that enhance packet treatment according to the expected energy performance, ultimately reducing the underlying expenditure of complex infrastructures in operation and permitting the provision of services at a lower cost and with higher efficiency levels. 6. Enabling slices of a network to go into sleep mode or operate at an energy-conserving pace is an essential technique in achieving energy efficiency in network infrastructures. In this context, developing enablers that allow slices of a network to go into sleep mode or operate at an energy-conserving pace can help reduce the energy consumption of networks, thereby promoting sustainability and reducing operational costs. 7. Moreover, dynamic resource allocation is a critical enabler in conserving energy in the network. This technique involves allocating resources to different network slices based on their current demand. When a network slice is not in use, the resources allocated to that slice can be de-allocated, which helps reduce the network's energy consumption.