FinOps: CPU, RAM, and Storage Cost Calculation in Hybrid Infrastructure
Effective IT cost management in a hybrid infrastructure is one of the key challenges for modern businesses. Companies utilizing a combination of on-premise data centers, private, and public clouds face the need for precise cost determination of each computing resource. Without a transparent pricing model, it's impossible to correctly allocate expenses, compare the efficiency of different environments, and justify costs. This article offers a practical approach to calculating the unit cost of CPU, RAM, and Storage, focusing on private virtualization systems as the most complex segment for pricing within FinOps and IT Financial Management (ITFM) practices.
Introduction to FinOps and Hybrid Infrastructure Challenges
In an environment where a company's IT infrastructure spans on-premise data centers, private virtualization, and multiple public clouds, the issue of cost accounting and allocation transcends a purely accounting task, transforming into a strategic problem. FinOps (Financial Operations) and ITFM (IT Financial Management) concepts become critically important for ensuring financial transparency and optimizing IT expenditures. While the costs of equipment purchased for a specific project are easily attributable, and public cloud costs are determined by the provider, private virtualization systems present the greatest complexity. Here, the price of a resource is not obvious and requires detailed calculation, considering hardware costs, redundancy, oversubscription ratios, and associated infrastructure. The goal of this approach is to create a model that not only calculates these figures but also enables their use for allocating costs to P&L centers, budgeting, and strategic planning.
Let's begin with a basic decomposition of the cost of a typical server, which forms the foundation for private virtualization. For simplicity, we will assume that the virtualization cluster consists of identical servers. Suppose each server is equipped with two CPUs, each with 64 cores, and 1024 GB of RAM. The total cost of such a server comprises several key components:
- Cost of memory (RAM).
- Cost of processors (CPU).
- Cost of the platform (motherboard, network cards, chassis, power supplies, and other components ensuring system functionality).
Knowing the total server cost and the price ratio of its main parts, allocation coefficients can be derived. For example, if K_CPU = 0.32, K_RAM = 0.5, and K_Platform = 0.18, this means 32% of the server's cost is attributed to the CPU, 50% to RAM, and 18% to the platform. These coefficients allow for determining the net cost of each component.
Server Hardware Cost Decomposition Methodology
To determine the base cost of a single CPU core and a single gigabyte of RAM, the aforementioned coefficients must be used. Let's assume Server_price is the full cost of the server. Then the net cost of components can be calculated as follows:
- Net cost of one CPU core (Core_c):
1 Core_c = (Server_price - (K_RAM Server_price + K_Platform Server_price)) / Number_of_Cores_in_Server
Using the coefficients and number of cores (64) from the original text, the formula would look like this:
1 Core_c = (Server_price - (0.5 Server_price + 0.18 Server_price)) / 64
- Net cost of one gigabyte of RAM (GB RAM_c):
1 GB RAM_c = (Server_price - (K_CPU Server_price + K_Platform Server_price)) / RAM_Volume_in_Server
Considering the coefficients and RAM volume (1024 GB):
1 GB RAM_c = (Server_price - (0.32 Server_price + 0.18 Server_price)) / 1024
These formulas provide an initial, raw cost for each resource. It's important to note that these calculations are basic and do not account for all nuances of real-world operation. However, they lay the groundwork for further refinement of computing resource costs.
Accounting for High Availability and Oversubscription Ratios
In real virtualization systems, additional coefficients must be factored in to reflect requirements for fault tolerance (HA - High Availability). These coefficients increase the effective cost of resources, as a portion of the capacity is reserved and cannot be used for primary workloads. For example, if 50% of CPU and 20% of RAM are reserved for HA, it means only the remaining resources are available for hosting virtual machines. Thus, the effective cost of each available core or gigabyte of memory increases.
Beyond high availability, an important parameter is the oversubscription ratio. Oversubscription is the ratio of allocated virtual resources to physical ones. For instance, a CPU oversubscription ratio of 2.5 means that 2.5 virtual cores can be allocated for every physical core. For memory, oversubscription is typically not used or applied with great caution due to its direct impact on performance. Considering these factors, the adjusted cost of resources will look as follows:
- Adjusted cost of 1 Core (1 Core price):
1 Core price = (1 Core_c + 1 Core_c * CPU_HA_Coefficient) / CPU_Oversubscription_Coefficient
For example, with 50% HA and an oversubscription ratio of 2.5:
1 Core price = (1 Core_c + 1 Core_c * 0.5) / 2.5
- Adjusted cost of 1 GB RAM (1 GB RAM price):
1 GB RAM price = 1 GB RAM_c + 1 GB RAM_c * RAM_HA_Coefficient
For example, with 20% HA (without oversubscription):
1 GB RAM price = 1 GB RAM_c + 1 GB RAM_c * 0.2
Applying these coefficients allows for a more realistic assessment of computing resource costs, taking into account stability requirements and infrastructure utilization efficiency. As a result, a certain number of virtual CPUs (vCPU) and GB of RAM can be derived from one typical server, which will then be allocated to P&L centers based on the calculated prices. For instance, from a server with 64 physical cores, 50% reservation, and an oversubscription ratio of 2.5, we get (64 (1 - 0.5)) 2.5 = 80 vCPUs. If the initial data were 107 vCPUs and 855 GB RAM, as in the example, this is already the result of applying all coefficients to the original physical resources.
Calculating Data Storage and Auxiliary Infrastructure Costs
In addition to CPU and RAM, a key resource in IT infrastructure is data storage. The cost of 1 GB of SAN (Storage Area Network) also requires a separate calculation, including adjustment coefficients. For example, a 30% coefficient might be factored in for redundancy, overhead, or other operational costs associated with SAN. The formula for calculation might look like this:
1 GB SAN price = SAN_price / (SAN_volume - SAN_volume * Adjustment_Coefficient)
Or, if the coefficient is applied to the final price:
1 GB SAN price = (SAN_price / SAN_volume) * (1 + Adjustment_Coefficient)
It's important to understand that data storage systems vary significantly by class (e.g., All-Flash, Hybrid, HDD), performance, and functionality, which directly impacts their cost. A detailed calculation for different SAN classes can be significantly more complex and requires a deeper decomposition of costs.
Beyond core computing resources and storage, the cost of auxiliary infrastructure, such as network equipment and data backup systems, must be considered. For example, the cost of using network equipment can be calculated based on the cost per port, but often for simplicity, it is evenly distributed among P&L centers, similar to utility costs. The cost of data backup systems, in turn, can be tied to the volume of data stored. If everything is included in backups, the added cost of data backup systems can be calculated based on the consumed data volume with a specific coefficient (e.g., 0.67, which might account for license costs, equipment, and operations).
It's important to note that the calculations presented here are simplified and aim to demonstrate basic pricing principles. In real-world conditions, the ratio of component costs, their configurations, and supply prices can vary significantly. Maintaining up-to-date resource costs requires regular review and adjustment of coefficients, based on market dynamics and internal needs. A comprehensive FinOps approach involves continuous monitoring and optimization of costs across all levels of the IT infrastructure.
Key Takeaways
- Cost Decomposition: Accurate IT resource cost calculation in hybrid environments requires detailed cost decomposition of hardware, especially for private virtualization.
- High Availability Accounting: Resource costs must include coefficients for ensuring high availability and fault tolerance, as a portion of capacity is reserved.
- Oversubscription Ratios: Applying oversubscription ratios (especially for CPU) optimizes physical resource utilization but requires correct accounting in pricing.
- Storage and Auxiliary Systems: Calculating the cost of data storage and auxiliary infrastructure (networks, data backup systems) is also critical for a complete cost picture and can be distributed proportionally or as utility services.
- Dynamic Model: The cost calculation model must be flexible and regularly updated to reflect current market prices, equipment configurations, and internal company needs.
— Editorial Team
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