Edge Network Resource Consumption Data Collection and Metering
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Solution Overview
Problem
Current computing systems face challenges in efficiently metering and billing resource consumption across diverse substrates, including on-premises and public cloud environments, due to difficulties in collecting and aggregating consumption data at edge networks, leading to issues like data loss, overburdened servers, and incorrect billing.
Innovation Solution
A system and method that collect resource consumption data at edge nodes, sending a compact version to a centralized location for analysis, allowing for real-time monitoring, flexible metering policies, and automatic data storage in case of network failures, with dedicated collectors for each service and a mechanism to correct metering errors and alert users of issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If resource consumption data is collected and aggregated at centralized servers from edge networks, then billing accuracy can be improved, but server processing load increases and data loss occurs during network failures
Solution Approach 1:
The patent applies preliminary action by having edge nodes collect and store resource consumption data locally before network failures occur. The system pre-processes and buffers data at the edge, ensuring data availability even when network connectivity is lost. This prevents data loss by maintaining local copies of consumption data that can be synchronized later when the network is restored.
Solution Approach 2:
The patent introduces an intermediary mechanism by implementing local buffering and queuing systems at edge nodes between the data collection point and the centralized server. This intermediary layer decouples the edge network from the centralized server, allowing data to be stored locally during network outages and transmitted when connectivity is restored, thereby preventing data loss while maintaining billing accuracy.
2Adaptability or versatility
If resource consumption data is collected from multiple edge networks and sent to centralized servers, then billing coverage can be improved, but server processing load increases
Solution Approach 1:
The patent applies segmentation by dividing the data processing workload between edge nodes and centralized servers. Edge nodes perform local data collection, filtering, and preliminary aggregation, sending only processed summaries to centralized servers. This segmentation reduces the processing load on centralized servers while maintaining comprehensive billing coverage across multiple edge networks and diverse substrates.
Solution Approach 2:
The patent implements partial action by having edge nodes perform only the necessary data collection and preliminary processing locally, rather than sending all raw data to centralized servers. This partial processing at the edge reduces the volume of data requiring centralized processing, thereby lowering server processing load while still achieving comprehensive billing coverage through the aggregated edge data.
3Measurement precision
If detailed resource consumption data is transmitted from edge networks to centralized servers, then billing accuracy can be improved, but network bandwidth consumption increases
Solution Approach 1:
The patent applies the extraction principle by removing unnecessary data elements before transmission. Edge nodes filter and aggregate raw consumption data, extracting only the essential metrics needed for billing purposes. This extraction process maintains billing accuracy by preserving critical consumption information while eliminating redundant data, thereby reducing network bandwidth consumption during data transmission.
4Productivity
If real-time resource consumption monitoring is implemented across edge networks, then service quality can be improved, but system complexity increases
Solution Approach 1:
The patent applies universality by implementing a standardized data collection and transmission framework that operates uniformly across diverse edge networks and substrates. The system uses common protocols and data formats that work across different environments, enabling real-time monitoring capability without proportionally increasing system complexity. This universal approach allows the same infrastructure to serve multiple purposes and environments.
Data Source
AI summary
Various embodiments disclosed herein are related to a non-transitory computer readable storage medium. In some embodiments, the medium includes instructions stored thereon that, when executed by a processor, cause the processor to identify, at an edge network, resource consumption data including a status that indicates whether a service hosted on a cluster of nodes on the edge network is powered on, a type of a resource being consumed by the service, a quantity of the resource being consumed by the service, and a time stamp associated with the resource being consumed by the service and provide, to a remote server in communication with the edge network, the resource consumption data. In some embodiments, the remote server meters resource consumption based on the resource consumption data.