IMS Control Function Assignment for Emission Reduction
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Solution Overview
Problem
Existing IMS network technologies fail to optimally assign IMS control function instances to endpoints, neglecting factors that influence environmental impact such as greenhouse gas emissions, leading to suboptimal energy usage and increased emissions.
Innovation Solution
A method that assesses and prioritizes IMS control function instances based on performance and emission metrics, considering energy consumption and emission rates from different energy sources, to assign instances that minimize emissions while meeting performance requirements, using an assignment controller that manipulates configuration data for endpoint-specific prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If multiple IMS control function instances are deployed geographically distributed throughout the IMS network, then signaling latency is reduced and service quality is improved, but greenhouse gas emissions increase due to energy consumption across distributed locations
Solution Approach 1:
The patent applies local quality by evaluating and selecting IMS control function instances based on their specific environmental characteristics (energy source types, emission rates) rather than treating all instances uniformly. The system assigns endpoints to instances with locally optimal environmental profiles, considering the energy mix and emission characteristics of each instance's location, thereby reducing overall emissions while maintaining service quality.
Solution Approach 2:
The patent changes the selection parameters for IMS control function instance assignment from purely performance-based metrics to include environmental metrics such as energy source composition and greenhouse gas emission rates. This parameter expansion allows the system to optimize for both service quality and environmental impact simultaneously by selecting instances with favorable emission characteristics.
2Reliability
If instance assignment prioritizes load balancing and geographic proximity, then network performance and reliability are improved, but environmental impact is neglected leading to suboptimal energy usage
Solution Approach 1:
The patent introduces dynamic environmental awareness into the instance assignment process by continuously evaluating energy source types and emission rates of available instances. The assignment mechanism dynamically adapts to environmental conditions, selecting instances with optimal energy profiles while maintaining load balancing and reliability requirements, thereby improving overall energy efficiency without sacrificing network performance.
Solution Approach 2:
The patent extends the functionality of the instance assignment mechanism to simultaneously optimize for multiple objectives: load balancing, geographic proximity, network reliability, and environmental impact. By integrating environmental metrics into the universal assignment framework, the system achieves multi-objective optimization that considers both technical performance and energy efficiency.
3Productivity
If existing assignment approaches focus on load balancing and geographic proximity, then signaling capacity is increased, but environmental sustainability is compromised due to lack of emission considerations
Solution Approach 1:
The patent applies preliminary action by pre-evaluating and cataloging the energy source types and emission rates of IMS control function instances before assignment decisions are made. This advance environmental assessment allows the assignment mechanism to select instances with optimal emission profiles from the outset, integrating environmental sustainability into the core assignment process rather than treating it as an afterthought.
Solution Approach 2:
The patent incorporates feedback mechanisms that consider environmental metrics in the instance selection process. By evaluating emission rates and energy source compositions of available instances and using this information to guide assignment decisions, the system creates a feedback loop that continuously optimizes for environmental sustainability while maintaining signaling capacity and service quality.
Data Source
AI summary
An assignment controller (160) identifies multiple instances of an IMS control function (210, 220) as being candidates for assigning to an IMS endpoint (110). The controller (160) also obtains a performance metric for each candidate instance that is a measure of the extent to which performance requirements for a signaling path of an anticipated or ongoing session of the endpoint (110) would be met if the instance were to be assigned to the endpoint (110). The controller (160) further obtains an emission metric for each candidate instance that is a measure of the extent to which the instance would produce greenhouse gas emissions if the instance were to be assigned to the endpoint (110), given the energy consumption and rate of emissions currently attributable to the instance (110). The controller (160) prioritizes the candidate instances based on these metrics, and controls assignment of one of the instances to the IMS endpoint (110) to be performed according to that prioritization.