CommC: A Multi-Purpose COMModity Hardware Cluster

The high costs of acquiring and maintaining high-performance computing (HPC) resources pose significant barriers for medium-sized enterprises and educational institutions, often forcing them to rely on expensive cloud-based solutions with recurring costs. This paper introduces CommC, a multi-purpose commodity hardware cluster designed to reduce operational expenses and extend hardware lifespan by repurposing underutilized computing resources. By integrating virtualization (KVM and Proxmox) and containerization (Kubernetes and Docker), CommC creates a scalable, secure, and cost-efficient computing environment. The proposed system enables seamless resource sharing, ensuring high availability and fault tolerance for both containerized and virtualized workloads. To demonstrate its versatility, we deploy big data engines like Apache Spark alongside traditional web services, showcasing CommC’s ability to support diverse workloads efficiently. Our cost analysis reveals that CommC reduces computing expenses by up to 77.93% compared to cloud-based alternatives while also mitigating e-waste accumulation by extending the lifespan of existing hardware. This significantly improves environmental sustainability compared to cloud providers, where frequent hardware turnover contributes to rising carbon emissions. This research contributes to the fields of cloud computing, resource management, and sustainable IT infrastructure by providing a replicable, adaptable, and financially viable alternative to traditional cloud-based solutions. Future work will focus on automating resource allocation, enhancing real-time monitoring, and integrating advanced security mechanisms to further optimize performance and usability.

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Publication Details

Journal
Future Internet
Published
2025-03-11
DOI
https://doi.org/10.3390/fi17030121
Citations
2
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
4.54
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article

CommC: A Multi-Purpose COMModity Hardware Cluster

Agorakis Bompotas, Nikitas-Rigas Kalogeropoulos, Christos Makris
2 citations
Future Internet
Parallel Computing and Optimization Techniques
4.54
article

CommC: A Multi-Purpose COMModity Hardware Cluster

Agorakis Bompotas, Nikitas-Rigas Kalogeropoulos, Christos Makris
article en
2 citations

Abstract

The high costs of acquiring and maintaining high-performance computing (HPC) resources pose significant barriers for medium-sized enterprises and educational institutions, often forcing them to rely on expensive cloud-based solutions with recurring costs. This paper introduces CommC, a multi-purpose commodity hardware cluster designed to reduce operational expenses and extend hardware lifespan by repurposing underutilized computing resources. By integrating virtualization (KVM and Proxmox) and containerization (Kubernetes and Docker), CommC creates a scalable, secure, and cost-efficient computing environment. The proposed system enables seamless resource sharing, ensuring high availability and fault tolerance for both containerized and virtualized workloads. To demonstrate its versatility, we deploy big data engines like Apache Spark alongside traditional web services, showcasing CommC’s ability to support diverse workloads efficiently. Our cost analysis reveals that CommC reduces computing expenses by up to 77.93% compared to cloud-based alternatives while also mitigating e-waste accumulation by extending the lifespan of existing hardware. This significantly improves environmental sustainability compared to cloud providers, where frequent hardware turnover contributes to rising carbon emissions. This research contributes to the fields of cloud computing, resource management, and sustainable IT infrastructure by providing a replicable, adaptable, and financially viable alternative to traditional cloud-based solutions. Future work will focus on automating resource allocation, enhancing real-time monitoring, and integrating advanced security mechanisms to further optimize performance and usability.

Future InternetVol. 17(3)
University of Patras (GR)
Openalex Percentile: Top 8%
Parallel Computing and Optimization Techniques
4.54
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