Right-Sizing Hybrid Multi-Cloud Infrastructure
Unlocking Agility and Cost Efficiency with Parallel Works
Introduction: The ROI of Right-Sizing HPC Systems
In an era of rapidly evolving HPC and AI workloads, traditional on-premises systems often fall short in delivering the agility, scalability, and cost-efficiency required to stay competitive. By adopting a flexible, "right-sized" infrastructure approach—integrating on-premises resources, single or multi-cloud deployments, and hybrid multi-cloud frameworks—organizations can more effectively navigate the complexities of modern computing demands.
Right-sizing an organization's infrastructure to include hybrid cloud is key to balancing performance with cost and offers flexibility that traditional environments cannot provide. The goal of right-sizing infrastructure is to optimize both capacity and capability to limit idle resources.
The Hybrid Multi-Cloud Environment: Drivers and Dynamics
Organizations that run HPC and AI workloads using on-premises infrastructure face several challenges:
- Capital expense: Setting up an on-premises HPC infrastructure requires significant upfront investment in specialized hardware, facilities, and cooling systems
- Maintenance and upgrades: On-premises HPC clusters necessitate ongoing maintenance and periodic upgrades, adding to the total cost of ownership
- Scalability: Scaling on-premises HPC resources to meet fluctuating demand can be challenging and expensive
- Expertise: Maintaining an on-premises HPC setup requires access to skilled personnel with expertise in high-performance computing
Economics of Hybrid Multi-Cloud Infrastructure
The two main economic benefits focus on capacity and capability:
- Capacity optimization: Using hybrid systems allows organizations to recover resources that would otherwise be idle in an on-premises environment
- Capability optimization: Requires matching workloads to the ideal heterogeneous resources, whether that is on-premises, in the cloud, or utilizing specialized resources such as GPUs or quantum processors
Real-World Use Cases
Capacity Example: Midsize Life Science Organization
A life science organization with a $5 million annual compute budget was only using 70% of their capacity, resulting in 30% idle infrastructure. By implementing ACTIVATE's cost control tools and right-sizing their infrastructure to a 90% target utilization across on-premises and cloud, they achieved:
- 20% saved capacity
- $1 million ROI savings
Capability Example: Large Manufacturing Organization
A manufacturing organization reduced their annual compute costs by 27% by using ACTIVATE to right-size workloads across hybrid multi-cloud heterogeneous compute resources, shifting from $5M static on-premises costs to $3.66M using optimized hybrid deployment.
How Parallel Works ACTIVATE Solves Hybrid Multi-Cloud Complexity
The Parallel Works ACTIVATE platform is a unified control plane for HPC & AI resources that simplifies optimizing hybrid multi-cloud infrastructures:
- Seamless Orchestration: Unified interface to both on-premises and cloud resources, removing the need for manual integration
- Capacity Optimization: Cost control features help determine whether workloads will run in an on-premises data center or burst into the cloud
- Capability Management: Supports heterogeneous resource management, allowing IT teams to easily provision and allocate resources to different workloads
User Testimonials
"Parallel Works ACTIVATE lets us focus on research, not the digital plumbing that supports it. We are using it to run a digital twin model of the Earth's thermosphere and ionosphere, which lets us research and predict space weather more effectively." — Jeff Steward, Principal Scientist at Orion Space Solutions
"Parallel Works ACTIVATE makes it easier for researchers to access their computing resources. ACTIVATE dramatically improves 'time to science' for bio-medically focused researchers in their efforts ranging from genomics and pathology to drug development." — Shilesh Shenoy, Senior Staff Scientist at Albert Einstein College of Medicine