ENTERPRISE AI ANALYSIS: POSSIBLE FUTURES FOR CLOUD COST MODELS
Navigating the Evolving Landscape of Cloud Costs for Scientific Discovery
Cloud computing is at an inflection point, with AI/ML demand dominating innovation. This report analyzes current cloud cost models, their challenges for scientific computing, and proposes future models to ensure continued support for discovery and science.
Executive Impact of Cloud Cost Model Evolution
The shift towards AI/ML-centric cloud innovation presents both opportunities and significant challenges for scientific research funding and resource access. Key trends include:
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Projected Cloud Revenue by 2028
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AI/ML CAGR (2024-2029)
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HPC Market Revenue 2024
Deep Analysis & Enterprise Applications
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Current Challenges
NIST Cloud Characteristics vs. Reality
Cost Model Comparison
Transparency & Trust
Proposed Future Models
Cloud cost models, originally designed for general business, struggle to support the unique needs of scientific computing due to resource contention, lack of transparency, and rigid payment structures. A significant challenge identified is the hidden costs associated with data egress.
80x
Estimated additional markup for egress bandwidth costs
The NIST definition of cloud computing emphasizes on-demand self-service, rapid elasticity, and measured service. However, in practice, chip shortages, quota systems, and opaque pricing models often contradict these ideals, particularly for HPC workloads requiring highly contended resources like GPUs, leading to a complex provisioning process.
Enterprise Process Flow
On-Demand Request
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Resource Contention & Shortages
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Manual Quota Request (Vendor Interaction)
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Delayed/Partial Provisioning
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Costs Incurred Without Resources
Existing cloud cost models—on-demand, reserved instances, and spot instances—each present tradeoffs in cost, availability, and flexibility. None perfectly align with sporadic, long-term scientific research needs, which often require dedicated resources for short, intense periods.
| Model | Cost Savings | Availability/Flexibility | Scientific Workload Fit |
|---|---|---|---|
| On-Demand | None | High initial flexibility, but prone to contention for GPUs | Small, non-critical tests, prototyping |
| Reserved Instances | Up to 72% | Guaranteed capacity for specific types, long-term commitment | Consistent, predictable workloads (less common in science) |
| Spot Instances | Up to 90% | Lowest cost, but pre-emptible, opaque termination | Small, fault-tolerant jobs, backfilling |
| Micro-Commitments (Proposed) | Moderate | Short-term dedicated resources, predictable pricing | Scaled experiments, specific project phases |
Lack of real-time cost visibility and complex billing structures erode user trust, often leading to unexpected spending. Introducing features like real-time cost meters and simplified APIs is crucial for empowering scientific users to manage budgets effectively and foster trust.
Impact of Opaque Billing on Scientific Research
A university research group experienced unexpected charges totaling $4000 due to unallocated resources during a GPU request, leading to budget overruns and discouraging future cloud adoption. This highlights the critical need for transparent, real-time cost reporting to prevent 'idle resource' billing.
Key Outcome: Decreased cloud adoption and wasted research funds due to lack of real-time cost visibility.
New models such as micro-commitments, rental of unused capacity, tax-incentivized fractal sharing, predictive scheduling, and re-emergent spot blocks offer pathways to better align cloud resources with scientific needs, emphasizing flexibility and community resource sharing.
Flexibility
The critical missing component in current cloud cost models for science
Advanced ROI Calculator: Optimize Your Scientific Computing Spend
Estimate potential savings and reclaimed researcher hours by adopting optimized cloud cost models and resource management strategies. Adjust the parameters below to see the impact.
Estimated Annual Savings
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Reclaimed Researcher Hours Annually
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Roadmap to Optimized Cloud Cost Models for Science
Our phased approach guides scientific institutions and cloud providers through the strategic implementation of new cost models and resource access strategies, fostering collaboration and maximizing research impact.
Phase 1: Needs Assessment & Pilot Programs
Identify specific scientific workloads, resource contention points, and budget constraints. Launch pilot programs for proposed micro-commitment and fractal sharing models with early adopters.
Phase 2: Cloud Provider Collaboration & API Integration
Engage cloud vendors to develop transparent APIs for real-time cost tracking and predictive scheduling. Integrate these APIs into scientific workflow managers and develop custom dashboards.
Phase 3: Policy Advocacy & Incentive Structure
Work with government bodies and funding agencies to create tax incentives for unused capacity sharing. Establish clear SLAs for scientific workloads that prioritize flexibility and predictability over rigid reservations.
Phase 4: Community Adoption & Iteration
Promote successful pilot models across the scientific community. Gather feedback, iterate on models, and continuously adapt to evolving cloud technologies and research needs.
Ready to Discuss a Sustainable Future for Scientific Cloud Computing?
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