Universal Metric for Utility Computing Resource Billing
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Solution Overview
Problem
Conventional utility computing models face challenges in accurately determining and costing computing resource usage, leading to confusion and inefficiencies for both service providers and end users due to the lack of granularity in resource metrics.
Innovation Solution
A method and system that measure and convert various computing resource types into a universal metric, known as the Workload Allocation Cube (WAC) unit, allowing for precise monitoring and billing of resource usage across a utility computing environment, incorporating cost adjustments and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple different computing resource metrics are monitored to provide granular usage information, then measurement precision is improved, but device complexity increases and confusion arises from the inability to relate different metrics to pricing
Solution Approach 1:
The patent merges multiple different computing resource metrics (CPU cycles, memory usage, storage capacity, I/O operations, network bandwidth) into a single unified metric called the Workload Allocation Cube (WAC) unit. This consolidation resolves the complexity issue by providing a common language for resource measurement that directly maps to pricing, while maintaining the granularity needed for accurate usage assessment through the multi-dimensional nature of the WAC metric.
Solution Approach 2:
The WAC unit serves as a universal metric that performs multiple functions simultaneously: it measures resource usage across different computing resources, relates to pricing models, provides billing information, and enables resource allocation decisions. This multi-functionality eliminates the need for separate monitoring systems for each resource type and pricing model, reducing overall system complexity.
2Device complexity
If a single metric such as CPU usage is used to simplify monitoring, then device complexity is reduced, but measurement precision deteriorates as it does not provide sufficient granularity for understanding resource usage of different applications
Solution Approach 1:
The patent segments the WAC metric into multiple dimensional components, each representing different computing resources (CPU, memory, storage, I/O, network). This segmentation allows the system to maintain simplicity through a single unified metric while preserving the granularity needed to understand resource usage patterns across different applications and resource types through the multi-dimensional structure of WAC.
Solution Approach 2:
The patent introduces a new dimension to resource measurement by creating the WAC unit that combines multiple resource metrics into a single multi-dimensional metric. This dimensional transformation allows the system to move from tracking separate one-dimensional metrics (CPU only) to a comprehensive multi-dimensional metric that captures resource usage across all computing resources while simplifying the monitoring interface.
3Measurement precision
If resource usage is monitored across many different computing resources to accurately reflect true resource usage, then measurement precision is improved, but ease of operation deteriorates due to confusion and inability to relate metrics to pricing
Solution Approach 1:
The WAC unit acts as an intermediary that bridges the gap between detailed resource metrics and pricing models. Instead of requiring users to directly interpret and relate multiple complex metrics to pricing, the WAC serves as a mediating layer that translates resource usage into a standardized unit that directly corresponds to billing rates, simplifying the operation of billing and allocation processes.
Solution Approach 2:
The patent changes the parameter representation by transforming multiple resource metrics into a single standardized parameter (WAC units) that can be directly used for billing and allocation. This parameter transformation maintains measurement precision by preserving the multi-dimensional information in the WAC structure while improving ease of operation through the simplified single-parameter interface for billing purposes.
Data Source
AI summary
A universal platform for utility computing that allows service providers to charge clients for the services rendered according to a dimensionless cross-platform universal metric. The platform measures or monitors six metrics commonly used in software computing (MHz for CPU usage, Mbytes for memory usage, Kbytes/sec for I/O, Kbytes/sec for LAN, Kbits/sec for WAN and Gbytes for storage) and applies appropriate weighting and conversion factors to each consumption value to a value in the universal metric that can be applied agnostically to any system or application. The metric can also take other controllable variables into account, such as real estate cost, tax jurisdiction and electrical power. The metric effectively a metric makes diverse computing resources comparable. The total value of the resources consumed by a user determines the cost charged for the use of the computing services.