Power Optimization for Distributed Computing Systems
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Solution Overview
Problem
Conventional power efficiency monitoring systems focus on individual computing nodes, failing to effectively optimize power usage across computing node groups, such as datacenters, which limits the ability to distribute computing tasks efficiently and balance power consumption.
Innovation Solution
A telemetry system and analytics cloud module that collect and analyze power behavior data from multiple computing nodes to optimize power consumption by redistributing tasks and balancing loads across nodes, using data mining techniques to identify patterns and provide optimization suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If power efficiency monitoring focuses on individual computing nodes, then detailed power metrics can be collected, but the ability to optimize power usage across computing node groups is limited
Solution Approach 1:
The patent combines individual node power monitoring with group-level power optimization by integrating telemetry systems that collect metrics from multiple nodes and feed them to analytics cloud modules. This merging enables both detailed individual node measurement and coordinated group-level optimization strategies.
Solution Approach 2:
The patent introduces analytics cloud modules as intermediaries between individual computing nodes and the optimization system. These modules aggregate power behavior data from multiple nodes, analyze patterns, and generate optimization suggestions, thereby enabling group-level optimization without losing individual node measurement precision.
2Productivity
If computing tasks are distributed across multiple nodes, then overall system productivity improves, but power consumption management becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms where analytics cloud modules continuously monitor power behavior data from computing nodes, analyze it to identify patterns, and generate optimization suggestions. These suggestions are fed back to the system to adjust task distribution, creating a closed-loop control system that manages power consumption complexity automatically.
Solution Approach 2:
The system enables self-service power optimization by using analytics cloud modules to automatically analyze power behavior data and generate optimization suggestions without requiring manual intervention. The system monitors, analyzes, and adjusts itself based on detected patterns in power consumption across distributed nodes.
Data Source
AI summary
An embodiment includes determining a first power metric (e.g., memory module temperature) corresponding to a group of computing nodes that includes first and second computing nodes; and distributing a computing task to a third computing node (e.g., load balancing) in response to the determined first power metric; wherein the third computing node is located remotely from the first and second computing nodes. The first power metric may be specific to the group of computing nodes and is not specific to either of the first and second computing nodes. Such an embodiment may leverage knowledge of computing node group behavior, such as power consumption, to more efficiently manage power consumption in computing node groups. This “power tuning” may rely on data taken at the “silicon level” (e.g., an individual computing node such as a server) and/or a large group level (e.g., data center). Other embodiments are described herein.