Abstract: Described herein are devices, systems, methods, and processes for intelligently managing power consumption in a network by allocating a power budget for packet processing. The power budget can be allocated based on criticality and/or the trust level of the flow. A network device may determine which subsets of features can be executed within the power budget for specific flows. Network devices can signal their capability to run features based on power consumption and adherence to the power budget, allowing for cooperative end-to-end power-based decision-making and policy enforcement. Network devices unable to run all features can select a subset of the features within their power budget and a viable path where other network devices can execute the missing features. Source route information can be added to indicate the path and missing features to be executed by network devices down the segment routing path.

Abstract: Described herein are devices, systems, methods, and processes for managing power consumption in network nodes by dynamically disabling and reenabling features based on real-time power consumption monitoring and historical data analysis. A machine learning model is trained using historical sensor data and historical feature data to predict power consumption and derive feature-to-power association data. The power budget is determined based on sustainability goals. Real-time power consumption is monitored, and features are disabled or reenabled based on their priorities and power consumption levels to maintain the power budget. The machine learning model is validated and updated using new historical data to improve its prediction accuracy and adaptability. The feature-to-power association data is distributed to network nodes and management systems for power management purposes.

Abstract: A method includes receiving, at a chaos level engine, initial input parameters. The method may further include, with the chaos level engine, determining scaled input parameters based on the initial input parameters. The scaled input parameters define how the initial input parameters effect a computing environment to be tested. The method may further include, with the chaos level engine determining a chaos level for performing a chaos experiment on the computing environment based on the scaled input parameters and sending the chaos level to the computing environment for the chaos experiment. The method may further include, with the chaos level engine, receiving, from the computing environment, feedback defining an impact caused by the chaos experiment created at the computing environment and an intended level of chaos.