# Future Sustainable Internet Energy-Defined Networking
## 3 Internet Energy-Defined Networking--Solution Architecture  
This paper proposes a new energy-aware functional architecture for the Future Internet, as depicted in Figure 1. It includes the placement of specific energy components (i.e., probes and instrumentation functions, energy operation functions) at each of the four layers (i.e., Service Operation Support, Management Operations Support, Compute Operations Support, and Connectivity Operations Support) and their interactions with other networking functions at each layer and between layers via systems capable of exposing APIs. It covers all Internet infrastructure segments: Edge-Access-Core-Clouds. It helps identify and specify a comprehensive set of design methods for making future Internet networking fully energy-aware, continuously minimizing and managing the energy footprint sustainably, which is this paper's main novelty and focus. Some of the design methods for making future Internet networking fully energy aware would require appropriate standardization across all network components (marked with (*) below), and some are more complex to implement/realize due to the high level of interworking of network comports (marked with (**) below). More specifically, this paper presents the design of the following comprehensive set of design methods and solutions that will collectively meet the expected net reduction in energy consumption:  
* (*) Efficient energy instrumentation definition and design (probes, monitoring, models, APIs /interfaces, guarantees); network-level energy calculus and analysis. * Accurate energy consumption model(s) and energy metrics: User demand and its characteristics and the decisions on how this is handled directly impact the infrastructure's energy consumption. This paper elaborates on an energy consumption model for connectivity and computational resources, which will assist in predicting and optimizing consumption for various service offerings. This paper will investigate the relationship of a wide range of operational parameters, such as packet rates, queue lengths, central processing units (CPUs)/storage utilization, and system load with energy consumption and running services. These models will subsequently guide network and service management decisions to derive greener configurations for the infrastructure and service. An initial energy model is depicted in Figure 2, and this model explicitly accommodates the network energy operations aimed at the following:  
* (**) Improving operational efficiencies for network operators through typical organization and the autonomicity of all energy functions throughout the separate network slices or domains. * (**) Adaptive resource management: Unlike most state-of-the-art offline and centralized solutions, this paper will present adaptive resource management approaches that execute at run-time. These will closely track connectivity and computation demand and regulate its distribution intelligently. More efficient resource utilization can be achieved, allowing part of the infrastructure to go into sleep mode or operate at an energy-conserving pace. Simultaneously, realistic performance and reliability constraints will be considered. In addition, workload orchestration to warrant the efficient use of computing resources will be considered. Energy-aware scheduling for executing different types of AI tasks [24] will also be needed based on heterogeneous resources, latency bounds, and autonomic network systems' increasingly automated management functions (e.g., the orchestration, self-configuration, and organization of energy control loops). * (*) (**) Energy-friendly networking: As indicated in prior studies [3; 17] on the energy profile of network devices, forwarding decision-making, i.e., table lookups, is the foremost energy-consuming process. Thus, this paper will investigate alternative schemes that circumvent expensive table lookups by encoding semantic forwarding information other than Internet Protocol (IP) addresses in packet headers for direct processing.