RG Solutions® uses a data model based on a Software Oriented Architecture. In this model there are three different tiers: File Server, Web Services, and Business Processes.
For each tier, the processing maintains a database that contains native system measures as well as application measures that is present for the resource. This results in a rich set of analytics that include operational and workload cost metrics as well as information for life cycle analysis. The information is high quality and can be used directly as input to a Chargeback or Showback system.
As part of the data formation, the processing maintains dynamic manifests of the costs associated with the processing. These manifests allow for an accurate cost allocation of shared resources. Since the processing is dynamically forming these cost manifests, the manifests will be updated as the processing conditions change.
For the different types of data input (Native, Application, Web Services, and Human), the product provides different data metrics. For Native data, the product will maintain the performance information in terms and measures associated with the platform as well as metrics that are common to all systems. The data model provides for a rich set of analytics that will not only provide cost information, but also includes operational, functional, workload, demand, and usage metrics.
Metering Technology
RG Solutions® uses a metering technology for all resource calculations. This allows for the comparison, measurement, and costing of heterogeneous systems. Metering technology is not new as a whole, but is new to computing systems. While many Capacity on Demand schemes are called metering, they are simple adding capacity when needed. This is not metering.
Metering is the ability to measure the use of a system over time, and to incorporate the power of the machine in that measure. This type of metering calculation is used by power companies. When they bill the actual usage of power to a customer it is expressed in kilowatt hours. For computing systems, the metering can be expressed in native measures (i.e. MIP Months) or normalized measures (i.e. Standard Months).
Business Workloads
The data model supports a full range of business centric workloads that are customizable for specific requirements. Resulting workload(s) can be based on a corresponding organizational structure, business unit, or other functions specific to the business.
For in-built processing, workloads are formed by using a series of rules. One set of rules allow for the formation of business objects based on the underlying technical measures. Once formed, there is another set of rules that allow for repetitive “folding” of workloads into other workloads using Activity Base Costing methodology.
Data Scope
The scope of data within the Enterprise Platform allows for supporting information for just about any possible situation. At the lowest level, the native data is maintained in one hour (or smaller) intervals for a specific data type (i.e. program, disk, etc.). This means that for something like a program instance, it is possible to see the hourly (or smaller) processing and cost associated with the instance.
One of the strengths of the data model is the ability to create newer “hybrid” data from technical data. This is done by using workload rules and Companion Data supplied to the product. The Companion Data is user specific and provides the ability to create dynamic manifests. The net result is the ability to report technical processing and costs in the terms the business understands, and to attribute the cost to various business units and/or clients.
Bill of IT
The data model supports the use of an intelligent Bill of IT for shared resource users. Unlike traditional Chargeback systems, the Bill of IT is based on Total Cost of Ownership rather than the nuances of the Tax Code. The net result is a stable, consistent, and fair system that determines the source and cost of shared resource usage.
Organizationally, the Bill of IT provides information pertaining to the cost of processing. Through dynamic manifests, information is provided regarding cost composition (i.e. cost per claim, cost per policy, etc.). This information is also useful for demand and cost analysis.
