# CommC: A Multi-Purpose COMModity Hardware Cluster
## 5 Applications, Use Cases, and Cost Analysis
### Cost Analysis  
In the previous subsection, several applications of the cluster were presented, showcasing its versatility and utility. In this subsection, we will focus on conducting a detailed cost analysis by estimating the expenses associated with running the same experiments on prominent cloud platforms, namely Google Cloud, Amazon Web Services (AWS), and Microsoft Azure. This analysis aims to compare the resource requirements of each experiment and their corresponding costs on these industry-leading platforms, providing insights into the economic efficiency of utilizing a commodity hardware cluster versus well-established cloud services. The analysis will focus on three key aspects: computational resources, storage, and the network. The computational resources aspect includes the number of required instances, the core count, and the RAM necessary for each experiment. Regarding storage, each experiment was allocated 512 GB, and this will be factored into the cost analysis for each cloud platform. However, network traffic costs--both inbound and outbound--will be excluded from the analysis, along with any additional costs associated with disk read and write operations enforced by certain services. This simplification will result in a slightly lower overall estimated cost for the experiments, but it ensures a more streamlined and focused cost comparison. Each cloud platform provides a cost estimator tool that allows users to estimate the necessary funds for their system based on the specific requirements of an experiment. However, this approach often results in a rough estimation of the cost, or a \"guestimate\", due to variations in how each service calculates expenses. For instance, differences in pricing models, regional availability, instance types, and additional fees for features, such as data transfer or specialized hardware, can cause discrepancies in the estimations. As a result, although these tools offer a useful starting point, they may not always reflect the precise costs incurred during actual usage. Therefore, we will analyze each axis separately. Firstly, for each cloud service, we selected the appropriate configuration and virtual machine (VM) type based on the resource requirements of each experiment. Table 4 presents the chosen specifications for each platform. It is evident that, in some cases, the RAM requirements slightly exceed the experiment's actual needs. However, we opted for the configuration that most closely aligned with the resource demands of the experiments \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Description** & \begin{tabular}{c} **Apache** \\ **Spark** \\ **Version(s)** \\ \end{tabular} & **Nodes** & **CPU** & \begin{tabular}{c} **RAM** \\ **(GB)** \\ \end{tabular} & \
\begin{tabular}{c} **Usage** \\ **(h)** \\ \end{tabular} &
\begin{tabular}{c} **Avg.** \\ **RAM** \\ **Usage** \\ \end{tabular} \\ \hline Research on NLP & 3.4.1 & 7 & 28 & 68 & 80 & 92\% \\ Research on NNs with TorchDistributor & 3.5.3 & 4 & 16 & 32 & 80 & 95\% \\ Spark Bayesian Networks [31] & 2.4.3, 3.4.1 & 7 & 28 & 68 & 40 & 87\% \\ Triangle Counting in Large Historical & 3.5.3 & 9 & 36 & 104 & 192 & 78\% \\ Community detection in large social & 3.5.3 & 13 & 28 & 80 & 144 & 91\% \\ Community detection in large social & 3.5.3 & 11 & 44 & 148 & 240 & 96\% \\ networks (part 2) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Resource allocation and usage exclusively for Apache Spark experiments. while ensuring the necessary computational capacity was met. This pragmatic approach balances cost efficiency and functionality, leveraging the most suitable VM types available on each platform. Table 4 provides details about the cost rate per hour of usage for each selected configuration. It is important to note that these rates may vary depending on the date of access or the user's geographic location. For this analysis, the cost rates were retrieved on 22 January 2025, from Greece, with the servers hosted in the Central Europe region.