Cloud Resource Discovery Using In-Use Datacenter Indicators
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Solution Overview
Problem
Cloud-based discovery of computing resources across multiple datacenters is inefficient and time-consuming due to the large number of API calls required, which can overwhelm the service provider's capacity and result in outdated information by the time discovery is complete.
Innovation Solution
Implementing initial API calls to identify in-use datacenters and performing pattern-based discovery within these datacenters, reducing the number of API calls needed to complete the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-based discovery queries all datacenters to ensure complete resource information, then measurement precision is improved, but loss of time worsens
Solution Approach 1:
The patent performs preliminary action by first querying a subset of datacenters to obtain initial resource information before conducting the main discovery process. This preliminary query identifies commonly used resources and enables optimization of subsequent discovery operations, reducing overall discovery time while maintaining completeness.
2Measurement precision
If cloud-based discovery makes a large number of API calls to cover all datacenters, then measurement precision is improved, but object-generated harmful factors worsen
Solution Approach 1:
The patent applies partial action by querying only a subset of datacenters (e.g., 5-10 commonly used ones) rather than all datacenters. This partial querying approach obtains sufficient resource information for most discovery operations without overwhelming the service provider's API capacity, thus avoiding throttling while maintaining measurement precision.
3Object-generated harmful factors
If cloud-based discovery is throttled to prevent capacity overload, then object-generated harmful factors are reduced, but productivity worsens
Solution Approach 1:
The patent performs preliminary action by first querying a subset of datacenters to obtain initial resource information before conducting the main discovery process. This preliminary query identifies commonly used resources and enables optimization of subsequent discovery operations, reducing overall discovery time while maintaining completeness.
Solution Approach 2:
The patent applies partial action by querying only a subset of datacenters (e.g., 5-10 commonly used ones) rather than all datacenters. This partial querying approach obtains sufficient resource information for most discovery operations without overwhelming the service provider's API capacity, thus avoiding throttling while maintaining measurement precision.
4Loss of time
If cloud-based discovery completes quickly by querying fewer datacenters, then loss of time is reduced, but measurement precision worsens
Solution Approach 1:
The patent performs preliminary action by first querying a subset of datacenters to obtain initial resource information before conducting the main discovery process. This preliminary query identifies commonly used resources and enables optimization of subsequent discovery operations, reducing overall discovery time while maintaining completeness.
Data Source
AI summary
An example embodiment may involve requesting and receiving, from a distributed computing platform, resource indicators that specify a set of resources provided by the distributed computing platform, wherein the set of resources is associated with a user identifier; parsing the resource indicators to locate datacenter indicators in the set of resources, wherein the datacenter indicators identify in-use datacenters of the distributed computing platform; and performing pattern-based discovery, within the in-use datacenters, of computing resources of the distributed computing platform that are associated with the user identifier.