Cloud migration:Three successful strategies for research institutions

The Cloud migration is the process of moving scientific data, applications, and research workloads from on-premises IT infrastructure—such as local servers—to cloud platforms, whether public, private, or hybrid.

The objective is to ensure that this transition takes place without compromising data integrity and while preserving one of the most important principles of scientific research: reproducibility. Every experiment must remain repeatable using the same datasets and computational environments.

There are five primary reasons why cloud migration has become increasingly attractive for universities and research organizations:

1. Borderless accessibility: Research teams are no longer confined to a single location. Cloud environments provide secure remote access for collaborators across the globe, making talent accessible regardless of geography.
2. Elasticity during peak workloads: The cloud allows institutions to scale resources instantly to support specific demands—such as processing thousands of images in a single day—and then scale them back when no longer needed.
3. Virtually unlimited storage scalability: Many scientific projects generate data volumes that exceed the capacity of physical infrastructure.
4. Elimination of hardware maintenance: Institutions no longer need to purchase, install, maintain, or upgrade data centers, as these responsibilities shift to the cloud provider.
5. Support for emerging technologies: Projects involving advanced artificial intelligence and machine learning often require compute capabilities that legacy on-premises infrastructure simply cannot provide.

Cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) identify multiple migration approaches, the six migration strategies, commonly known as the “6 Rs.”

For research institutions, however, three strategies consistently provide the best balance between cost efficiency, compliance, and computational performance.

This approach consists of moving applications from local infrastructure to the cloud without modifying their underlying code.

It is particularly suitable for institutional repositories, laboratory information management systems (LIMS), and established operational platforms.

Its main advantages include rapid deployment, minimal disruption for users, and enhanced resilience through geographically redundant backups.

However, it is rarely the optimal solution for intensive scientific workloads. Migrating high-performance computing clusters using technologies such as Message Passing Interface (MPI) without redesigning the architecture can result in poor performance and excessive cloud costs. In those cases, architectural optimization should take precedence over simple migration.

This is often the preferred approach when data sovereignty requirements are a priority. However, hybrid cloud is no longer the only option available for managing sensitive research data.

• Sovereign clouds. Historically, sensitive datasets such as clinical records were kept entirely on-premises to satisfy ethics committees and regulatory requirements. Today, sovereign cloud environments supported by contractual guarantees can ensure data residency within the European Union while maintaining strict GDPR compliance.
• Certified cloud environments. Public cloud platforms increasingly offer advanced certifications, including Spain’s National Security Framework (ENS), making them suitable for highly sensitive scientific and governmental workloads.
• Cloud services inside institutional data centers (CDP). Solutions such as AWS Outposts and Azure Stack extend public cloud capabilities into an institution’s own facilities. Researchers retain physical control over infrastructure while benefiting from the same tools, automation, and user experience available in the public cloud.

This hybrid approach allows institutions to temporarily leverage public cloud resources during large-scale research campaigns without overwhelming existing infrastructure.

Cloud-native platforms take a different approach by building research environments from scratch using federated infrastructures and managed cloud services.

This model is particularly well suited to European and international projects that require compliance with FAIR principles—Findable, Accessible, Interoperable, and Reusable—and collaborative research standards.

The advantages for research teams include:

• Reproducibility: standardized environments ensure that researchers obtain consistent results.
• Pipeline traceability: every action can be tracked from data acquisition to final analysis.
• Automatic scalability: infrastructure grows dynamically with data volumes without manual intervention.
• Simplified collaboration: new collaborators can be integrated quickly and securely without depending on rigid local systems.

Selecting the right strategy makes it possible to adopt the most appropriate cloud model while maximizing research agility and maintaining institutional compliance.

Cloud migration delivers strategic benefits that accelerate scientific discovery, support knowledge transfer to society, and improve operational efficiency.

The most significant benefits include:

• Scalability and flexibility: Massive computing power can be accessed on demand to support intensive experimentation.
• Collaboration and remote access: Distributed international teams can work together simultaneously within secure environments.
• Cost optimization and funding efficiency: Institutions avoid overinvesting in infrastructure that may remain underutilized, transforming technology spending into a more sustainable operational model.

Although the benefits are substantial, cloud migration also presents challenges that must be addressed carefully. Cybersecurity remains a primary concern, particularly when dealing with sensitive research data and compliance requirements such as the National Security Framework (ENS) and the General Data Protection Regulation (GDPR).

Cloud costs can also escalate rapidly without strong governance and continuous monitoring.

Finally, institutions must invest in talent development. Closing the cloud skills gap is essential to ensure technical teams can fully leverage modern platforms and advanced technologies.

A successful cloud migration roadmap should start with three essential stages.

• Assessment and prioritization: Analyze the systems, applications, and datasets that will be migrated, prioritizing them according to business impact and technical complexity.
• Architecture and governance design: Define a clear governance framework and determine whether a hybrid, public, private, or multi-cloud model is most appropriate.
• Pilot project and phased deployment: Adopt a gradual rollout strategy, validating security, performance, and operational resilience before scaling migration efforts across the institution.

While these are the core stages, successful cloud migration requires strategic planning and hands-on experience to ensure long-term success.

Given the complexity of academic and scientific environments, professional guidance can help institutions move forward with confidence and fully capture the competitive advantages that cloud migration provides.

Need to optimize your scientific infrastructure? Bolboreta Desarrolla , we help research institutions design and execute successful cloud migration roadmaps. Get in touch with our team of experts to discuss your specific needs.