The Case for a Cost-Effective General-Purpose Computer Cluster for Small Colleges


  • Stefano Colafranceschi Eastern Mennonite University
  • Emanuele de Biase Cornell Business School



HPC, computer cluster, virtualization, small-size college, student engagement


The computational capabilities of commercial CPUs and GPUs reached a plateau but soft-ware applications are usually memory-intense tasks and they commonly need/utilize most recent hardware developments. Computer clusters are an expensive solution, although reliable and versatile, with a limited market share for small colleges. Small schools would typically rely on cloud-based systems because they are more afford-able (less expensive), manageable (no need to worry about the maintenance), and easier to implement (the burden is shifted into the datacenter). Here we provide arguments in favor of an on-campus hardware solution, which, while providing benefits for students, does not present the financial burden associated with larger and more powerful computer clus-ters. We think that instructors of engineering/computer science faculties might find this a viable and workable solution to improve the computing environment of their school without incurring the high cost of a ready-made solution. At the basis of this proposal is the acquisition of inexpensive refurbished hardware and of a type1 VMware hypervisor with a free licensing, as well as of a custom-made web plat-form to control the deployed hypervisors. VMware is a global leader in cloud infrastruc-ture and software-based solutions. In particular, the adoption of a customized "Elastic Sky X integrated" as hypervisor together with Virtual Operating Systems installed in the very same datastore, would constitute an interesting and working proof-of-concept achieving a computer cluster at a fraction of the market cost.




How to Cite

Colafranceschi, S., & de Biase, E. (2021). The Case for a Cost-Effective General-Purpose Computer Cluster for Small Colleges. International Journal of Emerging Technologies in Learning (iJET), 16(04), pp. 4–13.