# CommC: A Multi-Purpose COMModity Hardware Cluster
###### 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. c 202  
Such barriers can be categorized into technological, organizational, and environmental barriers. Technological issues include security and privacy concerns, integration difficulties with existing infrastructure, and unreliable cloud service performance. Organizational challenges stem from limited IT expertise, financial constraints, and resistance to change, which hinder smooth adoption. Environmental barriers involve regulatory compliance, vendor lock-in, market pressure for rapid adoption, unreliable internet connectivity, and lack of trust in service providers. To overcome these obstacles, organizations must adopt strategic planning, invest in training, and carefully select cloud providers to maximize the benefits of cloud computing. Moreover, the current trend of research in this area is concentrated on high-performance systems, focusing on optimizing resource allocation, minimizing latency, and improving scalability in dynamic and heterogeneous environments. Advanced frameworks like EVRM [3] leverage cutting-edge technologies such as deep reinforcement learning to manage multiple virtual resources, ensuring efficient utilization of CPUs, memory, and bandwidth while adapting to fluctuating workloads. These innovations aim to address the limitations of traditional resource management approaches, enhancing system performance and reliability, especially in the context of virtualization and cloud computing. The Elastic Virtual Resource Management (EVRM) framework, which is the work of Wang et al. (2025) [3], incorporates four modules--monitor, analyzer, planner, and executor--to dynamically manage virtual instances. The framework employs a deep reinforcement learning-based resource allocation method, Deep Deterministic Policy Gradient-based Resource Allocation (DDPG-RA), to optimize the allocation of CPUs, memory, and bandwidth. This approach learns the complex dependencies between resources and application performance, enabling better adaptation to fluctuating demands. The demand for high-performance computing has increased dramatically, fuelled by advancements in big data analytics, artificial intelligence, and other computation-intensive workloads [4].