Digital Twin Energy Estimation for Elastic Network Scaling
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Solution Overview
Problem
Existing networks face inefficiencies in energy consumption due to overprovisioning, leading to idle resources and increased energy use, while under-provisioning can result in SLA violations and poor user experience, with inaccurate energy consumption prediction exacerbating these issues.
Innovation Solution
Implementing AI-driven elastic networks that dynamically adapt their architecture to meet traffic demand, using digital twins and machine learning to optimize energy consumption while maintaining SLAs and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network capacity is significantly expanded through overprovisioning to meet increasing user demands and SLAs, then network performance and reliability are improved, but energy consumption increases due to idle resources
Solution Approach 1:
The patent implements dynamic resource provisioning that automatically adjusts network capacity based on real-time demand conditions. The system transitions from static overprovisioning to dynamic scaling, where resources are allocated flexibly according to actual traffic patterns and SLA requirements, thereby maintaining reliability while reducing energy waste during low-demand periods
Solution Approach 2:
The system changes key operational parameters including provisioning levels, power states, and resource allocation based on monitored demand conditions. By dynamically adjusting these parameters rather than maintaining fixed high-capacity settings, the network achieves SLA compliance only when necessary, reducing overall energy consumption while preserving reliability during peak demand
2Use of energy by moving object
If the network is elastically scaled down to conserve energy, then energy consumption is reduced, but performance constraints and SLAs may be violated
Solution Approach 1:
The patent implements continuous monitoring and feedback mechanisms that track both energy consumption and SLA performance metrics. This feedback loop enables the system to detect when scaling down approaches SLA violation thresholds and automatically adjust provisioning levels accordingly, ensuring that energy conservation actions do not compromise service commitments
Solution Approach 2:
The system performs preliminary assessments and predictions about future demand patterns before making scaling decisions. By anticipating upcoming traffic surges or SLA-critical periods, the network can proactively maintain necessary capacity levels, avoiding both premature scaling down that would violate SLAs and unnecessary energy consumption
3Use of energy by moving object
If AI-driven elastic networking is implemented to dynamically adapt architecture to traffic demand, then energy consumption is optimized, but system complexity increases
Solution Approach 1:
The patent implements self-service AI-driven controllers that autonomously perform resource provisioning, scaling, and optimization decisions without requiring complex manual configuration or intervention. The system self-learns from operational data and automatically adapts to changing conditions, reducing the operational complexity burden while achieving sophisticated energy optimization
Data Source
AI summary
In one implementation, a device obtains telemetry data regarding a plurality of entities in a computer network. The device obtains energy consumption information for the plurality of entities in the computer network. The device trains a machine learning model to estimate energy consumption by an entity in the computer network, based on the telemetry data and the energy consumption information. The device uses the machine learning model to estimate an energy consumption for a digital twin of a particular entity in the computer network, to assess an estimated change in energy consumption by that entity were a certain action be performed in the computer network.