Host Server Energy Cost Insight for Underutilization Alerts
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Solution Overview
Problem
Private cloud computing environments face significant challenges in managing energy consumption and costs due to varying load conditions, as existing techniques are not applicable across different vendors and fail to provide proactive alerts for underutilized servers.
Innovation Solution
A system that collects energy consumption data from host servers using unified API agents, identifies underutilized servers, calculates energy costs, and determines actions to reduce costs, such as workload migration or power-saving modes, while providing alerts to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing energy management techniques are used, then energy consumption can be monitored, but they are not applicable across different vendors and fail to provide proactive alerts
Solution Approach 1:
The patent implements a unified energy management system that can collect and analyze energy consumption data from host servers of different vendors through standardized interfaces. The system performs multiple functions including data collection, analysis, alert generation, and recommendation provision within a single platform, making it universally applicable across diverse server environments without requiring vendor-specific implementations
Solution Approach 2:
The patent introduces an intermediary energy management system that acts as a mediator between diverse host servers and the monitoring/analysis functions. This intermediary layer standardizes interactions with different vendor servers, translating various server interfaces into a common format that the management system can process, thereby enabling multi-vendor compatibility without increasing overall system complexity
2Loss of information
If energy consumption data is collected from all host servers, then comprehensive energy cost calculation is enabled, but this requires unified API agents across different vendors
Solution Approach 1:
The unified API agents are designed to perform multiple functions: collecting energy consumption data, identifying underutilized servers, calculating energy costs, and generating alerts. This multi-functional approach consolidates what would otherwise require separate systems for each vendor, reducing overall API integration complexity while enabling comprehensive energy cost visibility across the entire server fleet
Solution Approach 2:
The system transforms raw energy consumption data from different vendors into standardized parameters and metrics that can be uniformly analyzed. By changing the parameters to a common format through the unified API, the system enables comprehensive energy cost calculation without requiring complex vendor-specific integration logic for each data type
3Loss of energy
If underutilized servers are identified and optimized, then energy costs are reduced, but this requires proactive monitoring and forecasting capabilities
Solution Approach 1:
The system performs preliminary actions by continuously monitoring energy consumption patterns and forecasting future energy costs before actual high-cost periods occur. It proactively identifies underutilized servers in advance, allowing optimization actions to be taken before energy costs peak, thereby reducing overall energy costs without requiring complex real-time control systems
Solution Approach 2:
The system implements feedback mechanisms that provide continuous information about energy consumption, server utilization, and cost projections to users. This feedback enables informed decision-making about resource allocation and server optimization, reducing energy costs through user-driven actions rather than requiring complex automated control systems
Data Source
AI summary
Respective energy consumption data is collected via respective agents running on respective host servers. The respective energy consumption data represents energy consumed by the respective host servers over a time period. The respective agents communicate with hardware on each of the respective host servers using a unified application programming interface (API). Respective energy costs are determined over the time period for the respective host servers based on the respective energy consumption data. A subset of the respective host servers that are being underutilized is identified based on the respective energy consumption data and the respective energy costs. An action to take with respect to the subset of the respective host servers that are being underutilized is determined to reduce the energy costs.