Rationale

The computing landscape has undergone a profound transformation over the past decade. Contemporary software systems are no longer deployed as standalone applications running on individual machines but as distributed services operating across cloud platforms, container orchestration systems, high-performance computing (HPC) clusters, and artificial intelligence (AI) infrastructures. Consequently, the competencies expected of Computer Science graduates have expanded beyond software development to include the deployment, operation, automation, monitoring, and scaling of complex computing infrastructures.

This Special Topics course, Scalable Computing Infrastructure for Cloud, Artificial Intelligence, and High-Performance Systems, is intended to expose undergraduate students to the operational technologies that underpin modern computing platforms. The course complements existing offerings in operating systems, computer networks, software engineering, and distributed computing by emphasizing the practical integration of infrastructure components that students will encounter in professional practice.

The offering of this course is motivated by several converging technological and educational trends.

1. Rapid Industrial Adoption of DevOps and Platform Engineering

Software development organizations have increasingly adopted DevOps practices to accelerate software delivery while improving reliability, scalability, and operational efficiency. Modern software engineering teams are expected to understand not only application development but also deployment pipelines, infrastructure automation, monitoring, containerization, and cloud-native operations.

A recent international study comparing higher education and industry practices concluded that several DevOps competencies remain significantly more prevalent in industry than in university curricula and recommended curricular adaptations to narrow this gap (Sanchez-Cifo et al., 2023; see also Alves & Rocha, 2021 and Fernandes et al., 2022).

2. Growing Gap Between University Curricula and Industry Needs

Several empirical studies have reported a mismatch between the technologies emphasized in university computing curricula and those demanded by employers. Recent analyses of university syllabi and software engineering job postings have shown rapidly increasing demand for cloud technologies, containerization platforms such as Docker and Kubernetes, and infrastructure automation tools, while these technologies remain comparatively underrepresented in undergraduate curricula (Dobslaw et al., 2023; Phan et al., 2026).

These findings suggest that graduates who possess practical experience with modern computing infrastructures have a competitive advantage in entering today's software engineering workforce.

3. Emergence of Cloud-Native Computing as the Dominant Deployment Model

Cloud computing has become the default deployment environment for enterprise applications, scientific computing platforms, and digital services. Modern software systems are increasingly built upon virtual machines, containers, orchestration frameworks, distributed storage, service discovery, and automated deployment pipelines.

Consequently, infrastructure engineering has evolved into an essential component of software engineering rather than a specialized administrative task. Graduates entering industry are now expected to understand the operational environments in which their software executes.

4. Increasing Infrastructure Requirements of Artificial Intelligence and High-Performance Computing

The rapid growth of AI has shifted attention from algorithm development alone toward the infrastructure required to train, deploy, and maintain large-scale machine learning systems. Distributed GPU clusters, high-speed networking, distributed storage systems, workload schedulers, and monitoring platforms have become integral components of modern AI ecosystems.

Likewise, scientific computing and HPC continue to rely on scalable computing infrastructures that integrate storage systems, resource schedulers, parallel file systems, and cluster management software. Introducing students to these technologies prepares them for careers spanning cloud computing, AI, data science, scientific computing, and computational research.

5. Growing Recognition of Hands-on Infrastructure Education

Recent research on DevOps education emphasizes that effective preparation for modern computing careers requires project-based and laboratory-intensive instruction. Researchers have identified collaborative, project-oriented, and deployment-focused learning experiences as among the most effective methods for teaching operational computing competencies and have called for greater emphasis on authentic infrastructure engineering experiences in higher education (Ferino et al., 2023).

Accordingly, this Special Topics course emphasizes hands-on deployment, systems integration, troubleshooting, infrastructure automation, and production-style laboratory exercises rather than purely theoretical discussions.

6. Alignment with Emerging Computing Curricula

The ACM Computing Curricula 2020 recognizes that computing programs must continuously evolve to address rapidly changing technologies and emerging areas of professional practice. Contemporary computing education increasingly encompasses cloud computing, distributed systems, cybersecurity, data-intensive computing, and AI infrastructure alongside traditional computer science foundations.

The Special Topics course aligns with these directions by integrating concepts from operating systems, computer networks, distributed systems, cloud computing, virtualization, DevOps, HPC, and AI infrastructure into a coherent systems-oriented learning experience. It therefore serves as a natural capstone-level integration course for students who have already completed the foundational Computer Science curriculum.

Future-Proofing UPLB Computer Science Graduates

One of the strategic directions articulated by the current University administration is the need to future-proof UPLB graduates by equipping them with competencies that remain relevant in a rapidly evolving technological landscape. This Special Topics course directly supports this objective by preparing students for infrastructure paradigms that are increasingly becoming standard across the software industry, cloud service providers, AI research laboratories, scientific computing centers, and technology startups.

Although no permanent undergraduate course currently exists within the BSCs curriculum that comprehensively addresses scalable computing infrastructure, offering this subject initially as a Special Topics course provides an agile mechanism for responding to rapidly emerging technologies while allowing the Institute of Computer Science to evaluate student interest, laboratory requirements, and industry relevance before institutionalizing the course as a regular elective.

Moreover, this Special Topics course leverages ICS' established strengths in operating systems, computer networks, parallel computing, distributed computing, scientific computing, and artificial intelligence. By integrating these areas into a single hands-on infrastructure course, UPLB can provide students with practical competencies that remain uncommon in many traditional Computer Science programs. Graduates who complete this course will have early exposure to technologies such as infrastructure automation, container orchestration, distributed storage, monitoring systems, cluster scheduling, and AI/HPC platforms—preparing them to contribute more effectively to multidisciplinary engineering teams and enhancing their competitiveness in both local and international technology sectors.

Literature Cited

Alves I. & C. Rocha. 2021. Qualifying Software Engineers Undergraduates in DevOps - Challenges of Introducing Technical and Non-technical Concepts in a Project-oriented Course. arXiv [cs.SE], doi: 10.48550/arXiv.2102.06662.
Dobslaw F., K. Angelin, L.-M. Oberg & A. Ahmad. 2023. The Gap between Higher Education and the Software Industry - A Case Study on Technology Differences. arXiv [cs.SE], doi: 10.48550/arXiv.2303.15597.
Ferino S., M. Fernandes, E. Cirilo, L. Agnez, B. Batista, U. Kulesza, E. Aranha & C. Treude. 2023. Overcoming Challenges in DevOps Education through Teaching Methods. arXiv [cs.SE], doi: 10.48550/arXiv.2302.05564.
Fernandes M., S. Ferino, A. Fernandes, U. Kulesza, E. Aranha & C. Treude. 2022. DevOps Education: An Interview Study of Challenges and Recommendations. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Software Engineering Education and Training, pp. 90 - 101, doi: 10.1145/3510456.3514152.
Phan H., I. Kuzminykh & B. Ghita. 2026. Understanding the Skills Gap between Higher Education Institutions and the Software Engineering Industry. arXiv [cs.SE], doi: 10.48550/arXiv.2604.26655.
Sanchez-Cifo, M.A., P. Bermejo & E. Navarro. 2023. DevOps: Is there a gap between education and industry? Journal of Software: Evolution and Process 35(12):e2534, doi: 10.1002/smr.2534.