Cloud Power Analytics Application for Energy Optimization

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Solution Overview

Problem

Manual solutions for monitoring and managing energy consumption across dispersed computing resources in cloud computing are not cost-effective, lacking efficient analytics for power consumption metrics.

Innovation Solution

A power analytics application that captures and analyzes power consumption data from servers, presenting metrics in a business intelligence structure, using machine learning to detect patterns and trends, and alerting stakeholders about outlier components or optimal energy usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual solutions are used for monitoring and analyzing cloud computing assets, then monitoring support can be provided, but it is not cost effective and difficult to scale across dispersed resources

Engineering Contradiction:

Improvemonitoring supportVSAvoidcost effectiveness

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The system enables self-service monitoring through automated agents deployed on cloud resources that autonomously collect, transmit, and analyze their own operational data without requiring manual human intervention for each monitoring task

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual monitoring operations are replaced with automated software agents and machine learning algorithms that perform data collection, transmission, and analysis functions previously requiring human components

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of manufacture

If automated monitoring systems are deployed across dispersed cloud resources, then cost effectiveness improves, but system complexity increases

Engineering Contradiction:

Improvecost effectivenessVSAvoidsystem complexity

Core Design Contradiction:

Ease of manufactureVSDevice complexity

Solution Approach 1:

The monitoring system is segmented into independent, modular agents that can be deployed individually on each cloud resource, allowing the complex monitoring function to be broken down into manageable, reusable components that simplify overall system implementation

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The monitoring agents are designed as universal, multi-functional components that can operate across different cloud platforms and resource types, reducing the need for multiple specialized systems and thereby simplifying the overall architecture

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If detailed power consumption data is collected from all server components, then measurement precision improves, but energy consumption for data collection increases

Engineering Contradiction:

Improvepower consumption metricsVSAvoidenergy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system collects detailed power consumption data from individual components only when necessary for specific analysis purposes, rather than continuously monitoring all components at maximum detail, thereby reducing overall energy consumption while maintaining measurement precision where needed

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The monitoring system dynamically adjusts the level of measurement detail and data collection frequency based on operational conditions, changing parameters such as sampling rate and granularity to optimize between measurement precision and energy consumption

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS9645629B2Providing energy consumption analytics of cloud based service

Publication Date: 2017.05.09 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Energy consumption analytics of a cloud based service is provided. An application such as a power analytics application monitors power consumption data of a server of the cloud based service. The power consumption data of the server is captured in a data file, a data store, a temporary storage, etc. The power consumption data is analyzed to detect power consumption metrics for the server. The power consumption metrics are presented in a business intelligence data structure.