Logging Service for Computing Resource Usage Measurement
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Solution Overview
Problem
Measuring usage of computing resources in cloud computing environments is challenging due to the complexity of detecting usage events, aggregating data, and generating large amounts of data, which can impact user experience and system efficiency.
Innovation Solution
A logging service generates event data that is transmitted to an event processing system, where it is stored as structured or semi-structured file system objects, allowing for parallel processing using map-reduce operations for de-duplication, aggregation, and reporting, with the ability to capture snapshots of resource states with minimal impact on resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed usage measurements are implemented to accurately track computing resource consumption, then billing accuracy and usage analysis improve, but system performance deteriorates due to the overhead of detecting and processing usage events
Solution Approach 1:
The patent applies preliminary action by pre-defining usage events and their associated metrics before actual resource consumption occurs. The system establishes measurement configurations that specify what to measure, how to measure it, and when to report, allowing the measurement framework to be prepared in advance without impacting real-time system performance during actual usage
Solution Approach 2:
The patent introduces an intermediary measurement agent that acts as a mediator between the computing resources and the billing system. This agent collects usage data, processes measurements, and generates reports without requiring direct intervention in the core computing operations, thereby isolating the performance impact to the intermediary layer rather than the critical computing paths
2Measurement precision
If comprehensive event data is collected and processed in real-time to monitor resource usage, then usage detection accuracy improves, but data processing complexity and computational overhead increase
Solution Approach 1:
The patent applies segmentation by dividing the measurement system into distinct modular components: event definition modules, data collection modules, processing modules, and reporting modules. Each module handles specific aspects of measurement independently, allowing for targeted optimization and reducing overall system complexity while maintaining comprehensive measurement capabilities
Solution Approach 2:
The patent utilizes parameter changes by allowing flexible configuration of measurement parameters such as sampling intervals, data retention periods, aggregation levels, and reporting frequencies. These parameters can be adjusted based on specific requirements to balance measurement accuracy with processing complexity, enabling the system to adapt to different operational contexts without redesign
3Loss of information
If detailed snapshots of resource state are captured frequently to track usage over time, then usage analysis capability improves, but impact on computing resources increases
Solution Approach 1:
The patent applies periodic action by implementing time-based sampling intervals for capturing resource state snapshots. Instead of continuous monitoring, the system periodically collects data at configured intervals, which reduces the frequency of measurements and associated overhead while still providing sufficient information for usage analysis and billing purposes
Solution Approach 2:
The patent applies partial action by selectively capturing only the specific resource metrics that are configured for measurement rather than monitoring all possible system parameters. This targeted approach collects sufficient information for billing and analysis needs without the overhead of comprehensive system-wide monitoring
Data Source
AI summary
To measure usage of computing resources on a computer, a logging service running on the computer generates event data. The computer transmits the event data to an event processing system. With a large number of computers, each computer transmits its event data to the event processing system. The event processing system stores the event data in a storage system as a file system object, such as a stream or file, in which different data fields of the received event data are stored in a structured or semi-structured manner. The event data can be processed in parallel on different pivots using map-reduce operations. Such processing can include, but is not limited to, de-duplicating event data, aggregating event data related to a resource into measurements of usage of that resource, and grouping original data or aggregated data by user or group of user for further analysis and reporting.