Cloud Infrastructure for Scalable Bioinformatics Workflows

Client: A US-based bioinformatics company

Domain: Life sciences / Bioinformatics

Platform: Google Cloud Platform

Core Technology: Galaxy Server

Challenge

The client, a US-based bioinformatics company, needed a production-grade compute platform to run analytical workflows for genomic and molecular data. Their workloads were inherently bursty — long periods of moderate activity punctuated by intensive computational runs that could saturate fixed infrastructure for hours or days.

Their existing setup suffered from three core problems:

1. No elasticity. Fixed on-premise resources meant either over-provisioning (expensive idle capacity) or under-provisioning (job queues backing up during peak demand, delaying research timelines).
2. Manual operations. Scaling required hands-on intervention — provisioning machines, configuring software, integrating them into the job scheduler. This consumed engineering time and introduced human error.
3. No cloud presence. The team had no existing cloud infrastructure, no IaC templates, and no operational playbooks for running their platform outside a traditional data centre.

They needed a solution designed and deployed from scratch — not a migration, but a greenfield cloud build — that could handle unpredictable, compute-heavy bioinformatics workloads without constant manual oversight.

Solution

We designed and deployed the complete cloud infrastructure on Google Cloud Platform, purpose-built around the Galaxy bioinformatics platform. The architecture was built to be production-grade from day one: automated, observable, and cost-efficient.

Galaxy Server was deployed as the central orchestration layer. Galaxy is an open-source platform widely adopted in life sciences for building, running, and sharing reproducible analytical workflows. It served as the user-facing interface and job scheduler, receiving workflow submissions and dispatching compute tasks to backend workers.

Compute workers were deployed as a GCP SLURM Power Save (MIG), configured with SLURM’s built-in power save mechanism. When a job is submitted, SLURM directly provisions a GCP VM from a pre-defined instance template. When the node sits idle, SLURM deletes it. No intermediate autoscaler, no metrics pipeline — the job scheduler itself decides when compute is needed — ensuring the client paid only for what they used.

Key design decisions included:

• SLURM-native scaling via job queue. Rather than relying on indirect metrics like CPU utilisation, the system uses SLURM’s native power save mechanism. When a job enters the queue, SLURM provisions a VM. When a node is idle, SLURM deletes it. This eliminates the lag and inaccuracy of metric-based autoscaling — the scheduler has perfect information about demand because it is the demand.
• GCP-native services throughout. We used SLURM Power Saves, instance templates, Cloud Storage, VPC networking, and IAM — avoiding third-party abstractions to minimise operational complexity and lock-in to non-standard tooling.
• Infrastructure as Code. The entire deployment was codified, making it reproducible, auditable, and version-controlled. Environment changes went through the same review process as application code.
• Separation of concerns. The Galaxy server handled orchestration and user interaction on dedicated, always-on infrastructure. Compute workers were stateless and ephemeral — they pulled jobs, executed them, returned results, and could be terminated at any time without data loss.

Architecture Overview



• Galaxy Server — persistent instance handling workflow management, user access, and job scheduling
• SLURM Workers — ephemeral, stateless compute nodes that scale between a defined minimum and maximum based on real-time CPU load
• Cloud Storage — durable storage for input datasets, intermediate files, and workflow outputs

Results & Impact

Elastic capacity without manual intervention. The autoscaling MIG eliminated the provisioning bottleneck entirely. Peak workloads that previously required days of queuing were absorbed automatically, with new workers available within minutes.

Cost efficiency. The client moved from a fixed-cost model (paying for idle capacity) to a usage-based model. Workers scaled to zero during quiet periods and ramped up only when real work demanded it.

Production-grade from launch. The infrastructure was deployed with proper IAM controls, network isolation, and codified configuration — not a prototype that needed to be hardened later, but a production system from the start.

Researcher autonomy. Scientists could submit large workflow batches through Galaxy without worrying about infrastructure constraints. The platform handled the rest.

Operational simplicity. By staying within GCP-native services and avoiding unnecessary abstraction layers, we delivered a system the client’s team could understand, operate, and extend without deep cloud engineering expertise.

This engagement was delivered as a greenfield infrastructure project — from architecture design through to production deployment — over a focused consulting engagement.