Smart solutions for clever minds

The Challenge

Kablamo was engaged by one of Australia's leading universities, home to some of the most advanced research and development projects in the country. The computing requirements across the University's various R&D departments were exceeding their current capacity, creating both bottlenecks during peak periods and wasted resources during quiet periods.

Academic workloads are inherently uneven, with high peak loads that stretch resources and then periods of time when infrastructure is largely unused. Universities have traditionally maintained their own computing infrastructure, but this requires large investment in hardware plus high maintenance and development costs. Researchers across 16 departments were constrained by the same fixed pool of on-premise compute, meaning that a genomics team running intensive simulations could block capacity for an engineering group needing to process experimental data. When one department hit a deadline, everyone else waited.

The University needed a solution that was cost-effective, easy to use, and fit within their existing practices for running and accessing cloud infrastructure without requiring researchers to be experts in cloud technology. The platform needed to provide on-demand access to compute resources while maintaining the security and access controls required for sensitive research data. Critically, researchers ranged from computational scientists comfortable with command-line tools to social science teams who had never provisioned a virtual machine, so the solution had to work for both groups without separate training tracks.

The Approach

Kablamo was engaged to deploy AWS Service Workbench (SWB), allowing the University to assess how it could be used by research departments. SWB enables researchers to securely store and share data without waiting for university computing facilities to become available. From an initial proof of concept that demonstrated the viability of the approach, it was quickly moved into production.

The technical approach included a continuous delivery pipeline with custom functionality that allowed the University's own team to deploy updates and new configurations without external support. A complete AMI building pipeline for research environments ensured that each department could have pre-configured machine images tailored to their specific software and tooling requirements. For example, a life sciences group could launch an environment with R, Bioconductor, and the university's licensed genome analysis suite already installed, while an engineering team would get a different image with MATLAB, finite element solvers, and GPU drivers ready to go. Researchers did not need to install or configure any of this themselves.

Custom IAM roles and policies provided secure, fine-grained access control so that researchers could only access the resources and data relevant to their projects. Data isolation was particularly important for groups working with health records or commercially sensitive industry partnerships, where accidental cross-department access could breach ethics approval conditions. Kablamo also provided Agile delivery training for University staff, transferring knowledge about iterative development and delivery practices so the internal team could maintain and extend the platform independently.

The platform provided clear reporting and cost management visibility that was previously absent. Each department could see its own usage and costs, enabling informed decisions about resource allocation and preventing the surprise bills that often accompany early cloud adoption in research settings. Budget alerts were configured per department so that a runaway batch job would trigger a notification before costs escalated.

The Results

Kablamo onboarded 80 researchers across 16 research departments onto the platform, providing highly supportable and scalable solutions for the University's research groups. Researchers gained on-demand access to compute resources that scaled with their workloads, eliminating the bottlenecks that had previously delayed research timelines. The cost management visibility transformed how departments planned and budgeted for computing resources, replacing opaque shared infrastructure costs with transparent, per-department usage reporting.

Researchers who previously waited days or weeks for compute capacity could now spin up a pre-configured environment in minutes and release it when the job finished, paying only for what they used. Departments that had been reluctant to try cloud computing adopted the platform once they saw peers running workloads successfully. The self-service model also freed the University's central IT team from a constant queue of provisioning requests, allowing them to focus on platform improvements rather than ticket-by-ticket support.

Looking Forward

This approach has resulted in an uplift in the internal DevOps team's capabilities and processes, along with developing the University's knowledge of Agile methodologies. The skills transfer embedded during the project means the University's internal team can now manage day-to-day operations, onboard new departments, and extend the platform's capabilities without starting from scratch.

As new research groups come online, the AMI pipeline makes it straightforward to build purpose-specific environments for new disciplines. The University is also exploring tighter integration with its research data management policies, so that data classification and retention rules are applied automatically when a workspace is created. Kablamo continues to provide ongoing Product Care support, ensuring the platform stays current with AWS service updates and evolves alongside the University's research ambitions.