Managing large-scale parallel workloads presents unique challenges beyond simply adding more machines. Success requires clear decision-making, effective automation, and early testing of complex components. The following flow outlines each step and its importance.
Start with the numbers
Before anything else, agree on the measurable goals: how many vCPUs, what budget, how fast jobs must start and finish, and how tolerant you are to failures. These numbers keep conversations practical. If you can’t measure it, it’s hard to improve it.
What to do, and Why
1. Define goals & NFRs
Document concurrency targets, scheduling latency, SLOs, and team budgets. Clear, specific goals ensure alignment across all stakeholders.
Document concurrency targets, scheduling latency, SLOs, and team budgets. Clear, specific goals ensure alignment across all stakeholders.
2. Split control plane and executors
Treat the control plane as the system’s core for APIs, policy, and billing, while executors handle compute and data tasks. Isolate cloud-specific logic with adapters to simplify future cloud integrations.
Treat the control plane as the system’s core for APIs, policy, and billing, while executors handle compute and data tasks. Isolate cloud-specific logic with adapters to simplify future cloud integrations.
3. Design the data plan
Store inputs and outputs in object storage. Use signed URLs, multipart uploads, and regional caches for frequently accessed files. Avoid relying on a single POSIX filesystem unless its limitations are well understood.
Store inputs and outputs in object storage. Use signed URLs, multipart uploads, and regional caches for frequently accessed files. Avoid relying on a single POSIX filesystem unless its limitations are well understood.
4. Pick the scheduler approach
Begin with a managed batch solution for quick deployment. For low-latency placement or specialized hardware, consider a custom or two-level scheduler.
Begin with a managed batch solution for quick deployment. For low-latency placement or specialized hardware, consider a custom or two-level scheduler.
5. Event model & telemetry
Configure workers to send status updates and assign a trace ID to each job. Maintain straightforward alerts, as excessive notifications are often disregarded.
Configure workers to send status updates and assign a trace ID to each job. Maintain straightforward alerts, as excessive notifications are often disregarded.
6. Spot strategy
Design tasks to be small or checkpointable. Use spot instances to reduce costs, but reserve guaranteed capacity for critical jobs to meet SLAs.
Design tasks to be small or checkpointable. Use spot instances to reduce costs, but reserve guaranteed capacity for critical jobs to meet SLAs.
7. Cost metering & governance
Implement early tagging and track job-level costs within the platform. Establish quotas and automated alerts to keep financial stakeholders informed.
Implement early tagging and track job-level costs within the platform. Establish quotas and automated alerts to keep financial stakeholders informed.
8. Proof-of-concept (PoC)
Prioritize testing the most challenging aspects, such as data throughput and scheduling latency. If these fail, other improvements will not compensate. Keep the proof-of-concept focused and rigorous.
Prioritize testing the most challenging aspects, such as data throughput and scheduling latency. If these fail, other improvements will not compensate. Keep the proof-of-concept focused and rigorous.
9. CI/CD and safe releases
Use canary deployments, automatic rollbacks, and frequent, incremental changes. Large-scale releases carry significant risk.
Use canary deployments, automatic rollbacks, and frequent, incremental changes. Large-scale releases carry significant risk.
10. Runbooks and automation
Develop concise runbooks and automate routine tasks. Reserve human intervention for complex issues.
Develop concise runbooks and automate routine tasks. Reserve human intervention for complex issues.
11. GenAI for ops
Leverage AI to recommend resource placement, draft runbooks, or provide cost estimates, but ensure all outputs are reviewed by humans and maintain an audit trail.
Leverage AI to recommend resource placement, draft runbooks, or provide cost estimates, but ensure all outputs are reviewed by humans and maintain an audit trail.
This framework is a guide, not a strict checklist. Engage operations and finance teams early to identify risks at various scales. Focus your proof-of-concept on the most critical risks, then iterate.
Quick checklist
- Clear separation: control plane and executors
- Event-driven worker updates and trace IDs
- Signed URLs, multipart transfers, and caches for IO
- Two-level scheduler or a proven autonomous scheduler
- Restartable tasks and spot mix with some guaranteed capacity
- Job-level cost tracking, quotas, and alerts
- Canary CI/CD and automated rollback
- Automated runbooks and simple anomaly alerts
- PoC validating data throughput and scheduling latency
Final thought
Begin with a small scope and prioritize testing the most challenging components. Once data movement and scheduling are addressed, further progress becomes more manageable. Involve the team, apply straightforward automation, and iterate to build a reliable, large-scale platform.
Begin with a small scope and prioritize testing the most challenging components. Once data movement and scheduling are addressed, further progress becomes more manageable. Involve the team, apply straightforward automation, and iterate to build a reliable, large-scale platform.
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