Energy Telemetry Engine for Dynamic Workload Placement
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Solution Overview
Problem
The proliferation of cloud services in large physical data centers significantly impacts the environment due to high energy consumption, prompting the need for sustainable practices that effectively manage workload placement across different cloud service providers to minimize energy impact.
Innovation Solution
An Energy Telemetry Engine (ETE) is used to dynamically place workloads by generating an Energy Efficiency Quotient (EEQ) for each data center, ranking host locations based on energy impact, and selecting suitable ones that meet performance and energy efficiency criteria, with machine learning mechanisms to optimize migration and update rankings over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workloads are placed in large physical data centers to provide cloud services, then service capacity and availability are improved, but energy consumption and environmental impact increase
Solution Approach 1:
The patent implements dynamic workload placement that adapts to changing energy conditions. The system continuously monitors energy efficiency metrics and dynamically migrates workloads between data centers based on real-time energy availability and efficiency data, allowing the system to optimize energy consumption while maintaining service capacity
Solution Approach 2:
The system changes the parameter of workload placement by incorporating energy efficiency metrics as a key selection criterion. By evaluating and comparing energy efficiency parameters across different data centers, the system selects optimal host locations that minimize energy consumption while maintaining required service levels
2Use of energy by moving object
If workloads are migrated between data centers to optimize energy efficiency, then energy consumption is reduced, but system complexity and migration overhead increase
Solution Approach 1:
The patent introduces an intermediary component that acts as a workload placement manager, which handles the complexity of energy optimization. This intermediary system coordinates workload migrations, manages energy efficiency metrics collection and analysis, and makes placement decisions, thereby isolating the complexity from the core cloud service operations
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor energy efficiency metrics and workload performance. This feedback loop enables the system to learn from migration outcomes and optimize future placement decisions, reducing the need for complex manual configuration while improving energy efficiency over time
Data Source
AI summary
This disclosure describes dynamically placing workloads using cloud service energy efficiency. The techniques include obtaining energy efficiency metrics (EEMs) that indicate the carbon footprint for different data centers of cloud service providers. In some configurations, an Energy Efficiency Quotient (EEQ) may be generated by an Energy Telemetry Engine (ETE) that indicates the energy efficiency for each data center/Point of Presence (POP) where a workload may be migrated/hosted. The ETE can be used to rank the different host locations (e.g., different data according to their EEQ. In some examples, one or more other metrics (e.g., latency, bandwidth, . . . ) may be used to identify any POPs that do not meet specified conditions (e.g., latency constraints, bandwidth constraints, . . . ). When a suitable host location is determined (e.g. a POP meets both the performance and EEQ specifications), the workload may be placed onto one or more resources of the selected data center.