Green Elastic Network Power Degraded Modes for SLA-Safe Energy Savings
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Solution Overview
Problem
Existing networks face inefficiencies due to overprovisioning, leading to excessive energy consumption and resource wastage, while maintaining service level agreements (SLAs), and discovering power degraded modes in large-scale networks is cumbersome for administrators.
Innovation Solution
Implementing an AI-driven elastic network that dynamically adapts its architecture to conserve energy by identifying and activating power degraded modes, using machine learning to optimize network configurations and reduce energy consumption without compromising SLAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If overprovisioning is used to meet increasing user demands and SLAs, then network capacity and reliability are improved, but energy consumption and resource wastage increase
Solution Approach 1:
The network dynamically adjusts its provisioning state based on real-time demand conditions. The system transitions between overprovisioned, appropriately provisioned, and underprovisioned states by activating or deactivating network entities, allowing the network to maintain reliability when needed while reducing energy consumption during low-demand periods
Solution Approach 2:
The system changes the operational parameters of network entities by activating or deactivating them based on demand. This parameter change allows the network to optimize between reliability and energy consumption by adjusting the active network capacity to match actual user demands
2Ease of operation
If network capacity is significantly expanded in anticipation of future growth, then network design and operation are simplified, but energy consumption increases due to idle resources
Solution Approach 1:
The system performs preliminary actions by pre-configuring and staging network entities in a deactivated state, ready for rapid activation when demand increases. This allows the network to maintain operational simplicity while avoiding continuous energy consumption of idle resources
Solution Approach 2:
The network system automatically manages its own capacity allocation by monitoring demand and autonomously activating or deactivating network entities, eliminating the need for manual intervention while optimizing energy usage
3Use of energy by moving object
If power degraded modes are activated to conserve energy, then energy consumption is reduced, but network performance may deteriorate
Solution Approach 1:
The system continuously monitors network performance metrics and user demand, using this feedback to determine when to activate or deactivate network entities. This feedback mechanism ensures that power degraded modes are only activated when they will not cause performance deterioration below acceptable thresholds
Solution Approach 2:
The system applies partial action by deactivating only the necessary network entities to achieve energy savings, rather than reducing capacity uniformly. This selective approach maintains sufficient network performance while optimizing energy consumption
Data Source
AI summary
In one implementation, a device obtains data regarding a networking entity in a computer network and a set of possible configurations for the networking entity. The device determines a power degraded mode of the networking entity comprising one or more configurations from the set of possible configurations. The device estimates an amount of energy savings associated with activating the power degraded mode of the networking entity. The device causes the power degraded mode of the networking entity to be activated based on the amount of energy savings estimated by the device.