Virtual Data Center Storage Cost Allocation Engine
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Solution Overview
Problem
Determining storage costs for virtual machines in a virtual data center is challenging due to the dynamic nature and vast number of logical disks and datastores, making it difficult for IT managers to accurately allocate costs based on storage capacities and capabilities.
Innovation Solution
A method and system that calculate datastore-base rates and total costs by creating a graph with logical disks and datastores, assigning costs proportionally to remaining storage capacity, and using pseudocodes to determine datastore-base rates and allocate costs to virtual machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to determine storage costs for each virtual machine, then cost accuracy can be achieved, but the complexity and time required for management increases significantly
Solution Approach 1:
The system automatically calculates and allocates storage costs without requiring manual intervention. The cost allocation engine autonomously processes datastore utilization data, applies pricing rules, and generates chargeback reports, enabling the system to serve itself rather than requiring IT managers to manually compute costs for each virtual machine.
Solution Approach 2:
The patent introduces a cost allocation engine as an intermediary component between the storage infrastructure and virtual machines. This engine acts as a mediator that automatically processes storage utilization data and distributes costs according to predefined rules, eliminating the need for manual calculation while maintaining accuracy.
2Measurement precision
If detailed tracking of each logical disk and datastore is implemented, then cost allocation accuracy improves, but the system complexity and processing time increases
Solution Approach 1:
The patent segments the storage cost allocation process into distinct operational phases: data collection from datastores, graph construction representing datastore-VM relationships, cost calculation using pricing rules, and result generation. This segmentation allows the system to process detailed tracking information efficiently by handling each phase separately rather than simultaneously.
Solution Approach 2:
The system performs preliminary actions by pre-defining pricing rules and cost allocation policies before the actual cost calculation occurs. By establishing the pricing framework in advance, the system reduces processing time during execution while maintaining accurate cost allocation based on actual datastore utilization.
Data Source
AI summary
Methods and systems allocate storage costs to virtual machines (“VMs”) in a virtual data center. Methods calculate a datastore-base rate based on datastore utilized-storage capacity in each LD and each LD-base rate when the datastore utilized-storage capacity and each LD-base rate are available. Datastore total cost is calculated by multiplying the datastore-base rate by the datastore utilized-storage capacity. Methods also use graph based methods to calculate datastore-base rates when the datastore utilized-storage capacity is unknown for each LD. The datastore-base rate associated with each datastore may then be used to calculate a VM storage cost of each VM hosted by a datastore.