Backend Resource Cost Calculation for Online Services
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Solution Overview
Problem
Determining backend resource costs for online service offerings is challenging due to the diverse and large infrastructure involved, making it difficult to identify when capacities should be upgraded, how monetary costs should be allocated, and how many resources a particular service consumes.
Innovation Solution
A method that gathers metrics on load and latency, fits curves to collected data points to project maximum operational load, calculates cost per request, and statistically analyzes system logs to identify and quantify backend resource costs across various systems involved in fulfilling requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data collection from all backend systems is performed, then measurement precision of resource costs is improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent segments the complex backend infrastructure into individual measurable components (systems, databases, networks, data centers) and assigns unique identifiers to track resource consumption by each service. This segmentation allows precise measurement of resource costs for each service offering by collecting data from discrete logging points throughout the distributed system architecture.
Solution Approach 2:
The patent introduces intermediary logging systems and data collection mechanisms that act as mediators between backend resources and service offerings. These intermediaries capture and aggregate resource consumption data without disrupting the core service operations, making the measurement process less intrusive and more manageable despite the system's complexity.
2Measurement precision
If detailed tracking of all backend resources is implemented, then backend resource cost calculation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements universal logging and tracking mechanisms that serve multiple functions simultaneously: monitoring resource consumption, identifying system usage patterns, and generating cost allocation data. This multi-functionality reduces the need for separate specialized systems for each measurement task, thereby managing complexity while maintaining detailed tracking capabilities across the diverse backend infrastructure.
3Productivity
If statistical analysis of all service requests is performed, then productivity in capacity planning is improved, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary actions by continuously collecting and pre-processing backend resource data during normal system operations. Logging systems capture resource consumption metrics in real-time and store them in accessible formats, so that when capacity planning analysis is needed, the data is already organized and ready for statistical evaluation, significantly reducing the time required for productivity-enhancing analyses.
Data Source
AI summary
Representative embodiments disclose mechanisms to calculate resource costs (e.g., processing, memory, storage and/or monetary costs) to provide an online service. The service is offered by a service provider that provides a plurality of different online services. To identify systems involved in providing a particular service, each request is associated with a unique identifier. The identifier is passed between systems and logged. Thus a trace of the unique identifier yields all systems involved in providing the service. A maximum operational load, calculated from operational data, is used to calculate a cost per request. A statistical sampling of the logged data is retrieved and the cost for each request in the sample is calculated based on a sum of the costs of the systems used to service the request. The cost of each system is the cost per request times the number of times the system is used to fill the request.