Hybrid Cloud Load Balancing via Clean Energy Routing
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Solution Overview
Problem
In cloud computing, load balancing in hybrid cloud environments often leads to uneven resource utilization, resulting in increased carbon footprints due to reliance on traditional energy sources, which is unsustainable as businesses shift towards more tasks in hybrid cloud environments.
Innovation Solution
A method and system that optimize energy resource usage by collecting real-time and historical data to route computing workloads to servers based on clean energy availability, ensuring key performance indicators and business targets are maintained while maximizing clean energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional load balancing is used in hybrid cloud environments, then resource distribution is achieved, but carbon footprint increases due to reliance on traditional energy sources
Solution Approach 1:
The system changes the routing parameters by incorporating clean energy availability data into the load balancing decision-making process. Instead of using only traditional metrics like server load and response time, the system dynamically adjusts routing based on real-time clean energy availability at different data center locations, thereby reducing carbon footprint while maintaining productivity
Solution Approach 2:
The system introduces an intermediary layer that collects and processes clean energy availability data from multiple data center locations. This intermediary component acts as a mediator between the workload routing decisions and the energy sources, enabling informed decisions about where to route workloads based on environmental criteria without compromising operational efficiency
2Object-generated harmful factors
If workloads are routed based on clean energy availability, then carbon footprint is reduced, but system complexity increases due to data collection and routing optimization
Solution Approach 1:
The system achieves multi-functionality by integrating multiple capabilities into a unified load balancing framework: traditional load balancing functions, real-time data collection from multiple sources, clean energy availability monitoring, and dynamic routing optimization. This universal approach consolidates what could be separate complex systems into a single coordinated mechanism
Solution Approach 2:
The system implements feedback loops where real-time clean energy availability data is continuously collected from data center locations and fed back into the routing decision-making process. This feedback mechanism enables dynamic adjustment of workload routing based on current energy conditions, allowing the system to adapt automatically without requiring complex manual intervention
3Use of energy by moving object
If real-time data collection is implemented for energy optimization, then clean energy usage is maximized, but data processing requirements increase
Solution Approach 1:
The system extracts only the critical data elements needed for routing decisions - specifically clean energy availability metrics from various data center locations. Rather than collecting and processing all possible operational data, the system selectively extracts and processes only the energy-related information necessary for making informed routing decisions, thereby minimizing data processing requirements while maximizing clean energy utilization
Data Source
AI summary
A method, computer program product, and computer system are provided for load balancing in a hybrid cloud environment through optimization of energy resources. Real-time and historic data corresponding to a computing workload, one or more servers at one or more locations, and one or more clean energy sources accessible by the one or more servers at the one or more locations are collected. One or more key performance indicators, thresholds, or targets of a business associated with the computing workload are determined. The computing workload is routed to one or more servers at a location from among the one or more locations based on maximizing usage of clean energy from the one or more clean energy sources without affecting the key performance indicators, thresholds, or targets of the business.