Cloud Resource Allocation via Usage Data Feedback
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Solution Overview
Problem
Cloud computing service providers face inefficiencies in managing resources due to the lack of accurate prediction of usage patterns, leading to suboptimal allocation of resources and potential unauthorized usage detection.
Innovation Solution
The sharing of estimated and actual usage data between cloud service providers and recipients, combined with machine learning algorithms, allows for the determination of usage ratios and categorization of applications, enabling more precise resource allocation and detection of unauthorized usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud services provider allocates resources based on conventional monitoring methods, then resource allocation is simple to implement, but resource allocation accuracy is low
Solution Approach 1:
The patent implements feedback by collecting actual usage data from cloud services recipients and comparing it with estimated usage data. The system continuously monitors resource consumption patterns and adjusts allocations based on the difference between estimated and actual usage, creating a closed-loop control system that improves accuracy over time
Solution Approach 2:
The patent applies preliminary action by requiring cloud services recipients to submit estimated usage data before resources are allocated. This advance information allows the provider to pre-configure resource allocations based on predictions, and then refine allocations after actual usage is observed and compared
2Reliability
If cloud services provider monitors all usage details, then detection of unauthorized usage is improved, but loss of time for data processing increases
Solution Approach 1:
The patent extracts only the essential information needed for detection by having recipients submit structured output files containing specific metrics about resource usage. Rather than monitoring all raw data, the system focuses on key indicators that signal unauthorized usage, filtering out unnecessary information to reduce processing time
Solution Approach 2:
The patent replaces manual or conventional monitoring mechanisms with automated analysis systems that process usage data. Machine learning algorithms and automated comparison systems substitute for time-consuming manual review, enabling rapid detection of unauthorized usage patterns while maintaining high reliability
3Productivity
If cloud services provider apportions resources without usage data sharing, then ease of operation is maintained, but productivity of resource management decreases
Solution Approach 1:
The patent creates a universal data sharing framework that serves multiple functions simultaneously. The same usage data collection mechanism supports both resource allocation decisions and unauthorized usage detection, while the feedback loop serves both to improve allocation accuracy and to enable performance optimization across the cloud environment
Solution Approach 2:
The patent implements self-service by having cloud services recipients automatically submit their own usage data and estimated usage information. This automated self-reporting mechanism eliminates the need for complex provider-side monitoring infrastructure, maintaining operational simplicity while enabling data-driven resource management that improves productivity
Data Source
AI summary
Methods and systems are disclosed for improvements in cloud services by sharing estimated and actual usage data of cloud services recipients with the cloud services provider. The sharing of this data allows the cloud services provider to better apportion cloud resources between multiple cloud services recipients. By analyzing information included in the shared data (e.g., information about one or more applications that use the cloud resources), the cloud services provider may categorize the applications and/or the functions of those applications into authorized and unauthorized uses, the determination of which, is used to further efficiently apportion the cloud services resources.