Future-Proofing R&D: Migration to High-Availability Cluster Architecture
Industry
Life Sciences / Enterprise IT
Scope
Infrastructure as Code / Containerization (Incus) / High Availability / IT/OT Convergence
Timeframe
6 months
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10
Strategic Criteria Trade-off analysis criteria used to select the optimal Incus engine.
-
6
Consensus Global IT, OT, and Innovation teams aligned on the new standard
-
100%
Consensus – Alignment achieved across global IT, OT, and Innovation teams on the future standard.
01
CLIENT
A forward-thinking biotechnology organization modernizing its global R&D infrastructure looking to transition from legacy, isolated workstations to a robust, cloud-ready ecosystem.
02
BUSINESS NEEDS
The business required a scalable, resilient architecture that could support advanced data orchestration and be easily managed remotely, without disrupting ongoing experiments. The existing infrastructure relied on standalone Industrial PCs (IPCs), creating Single Points of Failure (SPOF).
03
CHALLENGE
To help our client achieve its goals, we overcome the following challenges:
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Fragile Legacy Stack
Dependence on individual machines meant that a hardware failure could halt data collection. -
Maintenance Overhead
Updating software across dispersed machines was slow and risky. -
Architectural Selection
Balancing the need for "Cloud Native" standards with the specific constraints of on-premise lab equipment.
04
SOLUTION
We led the architectural transformation from single-instance machines to a resilient Incus Cluster environment, utilizing containerization to decouple software from hardware. The solution entailed:
- Cluster Architecture Strategy Conducted a trade-off analysis of 7 architectural approaches, selecting Incus/LXC for its optimal balance of maturity, maintenance efficiency, and performance.
- High Availability (HA) Designed a clustered environment where workloads can migrate between nodes, eliminating downtime due to hardware failures.
- Containerized Orchestration Encapsulated lab applications into lightweight containers, facilitating rapid deployment, testing, and rollbacks.
- Infrastructure Standardization Established a template for global deployment, ensuring consistent configuration across all R&D sites.
By implementing the Incus Cluster architecture, we have established a 'Self-Healing Lab' environment where computing resources scale fluidly with our scientific needs, ensuring mission-critical resilience across our R&D operations.
Jacek Fischbach
Delivery Executive
Technology used
05
OUTCOME
The project established a new standard for the client's laboratory computing, aligned with modern DevOps practices. It fundamentally transformed the client's operational capabilities, ensuring resilience and readiness for future growth.
- Resilience Elimination of Single Points of Failure through clustered redundancy.
- Operational Efficiency Drastic reduction in time required for system updates and patching.
- Scalability Architecture ready to absorb new workloads (AI/ML models) without hardware reconstruction.
06
IMPLEMENTED SOLUTION
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10 Strategic Criteria
Used in the trade-off analysis to select the Incus engine based on maturity and cost. -
N+1 Redundancy
Target architecture designed to ensure zero downtime and eliminate single points of failure. -
6 New Techs
Infrastructure prepared to host next-gen data streams (BioHT, MAST, Vi-CELL Blu, etc.). -
100% Consensus
Alignment achieved across global IT, OT, and Innovation teams on the future standard.
-
10
Strategic Criteria Trade-off analysis criteria used to select the optimal Incus engine.
-
6
Consensus Global IT, OT, and Innovation teams aligned on the new standard
-
100%
Consensus – Alignment achieved across global IT, OT, and Innovation teams on the future standard.
