# CommC: A Multi-Purpose COMModity Hardware Cluster
## 5 Applications, Use Cases, and Cost Analysis
### Cost Analysis
These factors may slightly influence the overall cost and should be taken into consideration when interpreting the results. \[\text{Cost}=\text{Number of Instances}\cdot\text{Cost Rate}\cdot\text{Usage Hours} \tag{1}\]  
The costs of each experiment regarding the computational aspect were calculated as shown in Equation (1). The results are depicted in Table 5. The table summarizes the estimated costs for running the experiments on three major cloud platforms: Google Cloud, Azure, and AWS. Each experiment is listed, along with its corresponding cost on each platform, calculated based on the VM configurations and usage hours. The total cost for all experiments is also provided, highlighting the differences in cost efficiency among the platforms. The aforementioned cost is considered the monthly cost of the service used to conduct each experiment. Each service offers various storage options. However, we chose a persistent storage SSD of 512 GB, with the cost depicted in the last row of Table 5.  
Our cluster comprises 16 computers, each with a maximum power supply of 200 watts. This results in a total energy consumption of 3.2 kWh for the entire cluster per hour. In a worst-case scenario, where all systems operate at full capacity continuously for 24 h a day over a month (720 h), the total cost would amount to \(3.2\cdot 24\cdot 30\cdot 0.15=345.6\epsilon\), where 0.15 is the average cost of 1 kWh in Greece. This duration is more than sufficient to complete the required experiments, and the overall cost remains significantly lower compared to alternative cloud platforms. If commodity hardware were not utilized, the alternative approach would require the acquisition of pre-configured enterprise systems from major vendors, which comes with \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **GC** & **Azure** & **AWS** \\ \hline
**VM Type** & cta-standard-4 & B4 as & tdg.xlarge \\
**Cores** & 4 & 4 & 4 \\
**RAM (GB)** & 16 & 16 & 16 \\
**Base Core Frequency (Ghz)** & 3 & 2.45 & 2.5 \\
**Cost Rate (G)** & 0.21 & 0.165 & 0.13 \\ \hline \hline \end{tabular}  
* Cost rates and specifications may vary depending on the time of access.  
\end{table} 
Table 4: Cost comparison of VM types across different cloud services. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Description** & **Google (G)** & **Azure (G)** & **AWS (G)** \\ \hline Research on NLP & 117.6 & 92.4 & 75.26 \\ Research on NNs with TorchDistributor & 67.2 & 52.8 & 43.00 \\ Spark Bayesian Networks [31] & 58.8 & 46.2 & 37.63 \\ Triangle Counting in Large Historical Graphs [32] & 362.88 & 285.12 & 232.24 \\ Community detection in large social networks (part 1) & 393.12 & 308.88 & 251.59 \\ Community detection in large social networks (part 2) & 554.4 & 435.6 & 354.81 \\ \hline
**Total** & **1554.00** & **1221.00** & **994.56** \\
**Total + Storage 1** & **1564.24** & **1343.882** & **1006.34** \\ \hline \hline \end{tabular}  
* Total cost, including a 512 GB Standard SSD across cloud platforms. 2 Azure costs might include additional features or configurations.  
\end{table} 
Table 5: Estimated costs for running experiments on Google Cloud, Azure, and AWS. significantly higher costs. Systems such as the Dell PowerEdge R7525, HPE ProLiant DL385 Gen10 Plus, Lenovo ThinkSystem SR645, and Supermicro A+ Server offer high-performance configurations, typically featuring 128 to 240 threads, 256-512 GB RAM, and 8-16 TB of SSD storage. However, these solutions come at a steep price, ranging from EUR 15,000 to over EUR 30,000, depending on CPU configurations, memory capacity, storage options, and vendor support packages. For instance, a Dell PowerEdge R7525 with 128 cores, 256 GB RAM, and 8 TB NVMe storage can cost between EUR 18,000 and EUR 25,000, while an HPE ProLiant DL385 Gen10 Plus with similar specifications ranges from EUR 20,000 to EUR 30,000. These prices vary further based on warranties, performance tiers, and additional enterprise support options.