Green Load Balancer for Energy-Efficient Cloud Service Execution
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Solution Overview
Problem
Current data processing services lack a dynamic solution that considers sustainability aspects, such as energy efficiency and carbon intensity, when distributing tasks across computing resources.
Innovation Solution
Implementing a method for energy-efficient execution of services in a network, using a Green Load Balancer and Scheduler that select service instances based on energy efficiency, carbon intensity, and cost, while allowing for delayed execution to optimize energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If services are distributed across regionally distributed computing resources to improve sustainability, then energy efficiency is improved, but task execution time and latency may increase
Solution Approach 1:
The load balancing device dynamically selects service instances based on real-time energy profile data and task characteristics. The system continuously monitors and adapts its routing decisions to optimize energy efficiency while considering task-specific requirements such as latency sensitivity, transforming a static load balancing approach into a dynamic one that responds to changing energy conditions.
Solution Approach 2:
The invention introduces energy profiles as a new parameter for service instance selection, complementing traditional parameters like latency and cost. By querying and utilizing energy profile data from the emissions estimator, the system changes the selection criteria to include carbon intensity and energy consumption metrics, enabling energy-efficient routing without completely abandoning other performance parameters.
2Productivity
If load balancing is optimized for performance and latency, then task processing speed is improved, but energy consumption increases
Solution Approach 1:
The load balancing device modifies its selection parameters to include energy efficiency metrics alongside traditional performance metrics. By querying energy profiles from the emissions estimator and incorporating carbon intensity data into the selection criteria, the system balances productivity requirements with energy consumption considerations, moving beyond pure performance optimization.
Solution Approach 2:
The system dynamically adjusts service instance selection based on real-time energy profile data and task characteristics. For time-critical tasks, the system may prioritize latency-optimized instances, while for non-time-critical tasks, it selects energy-optimized instances, creating a dynamic balance between productivity and energy efficiency based on specific task requirements.
3Ease of operation
If service instances are selected based on cost and availability, then operational efficiency is improved, but carbon footprint increases
Solution Approach 1:
The invention extends the service instance selection criteria to include carbon intensity and energy consumption metrics in addition to cost and availability. The load balancing device queries energy profiles from the emissions estimator and incorporates these environmental parameters into the selection decision, enabling operators to optimize for carbon footprint reduction while maintaining operational efficiency.
Solution Approach 2:
The emissions estimator provides feedback about the carbon intensity and energy consumption characteristics of different service instances and regions. This feedback loop enables the load balancing device to make informed selections that reduce carbon footprint while maintaining operational efficiency, as the system continuously receives and utilizes emissions data to optimize routing decisions.
Data Source
AI summary
The proposed solution aims to extend load balancing and scheduled execution of services in the cloud to the principles of "green engineering." Standardized APIs, algorithms, and supporting services are defined to implement load balancing and scheduling according to the so-called "green" efficiency criteria listed above. The idea is to select provider service instances based on their energy efficiency, rather than simply balancing latency and availability. Furthermore, customers can enable delayed execution of the required operation by providing an execution profile, for example, when the execution is not time-critical and can be postponed, such as a backup or other non-time-critical background service.