Data Center Workloads Overview
Workloads are the applications and processes that consume compute, storage, and network resources in data centers. This overview defines the major workload families, their infrastructure needs, and how they map to facility types and deployment models across DatacentersX.
What is a Workload?
- Definition: A cohesive set of jobs/services with common performance, resiliency, and compliance requirements.
- Dimensions: latency, throughput, concurrency, data gravity, burstiness, confidentiality, compliance.
- Outcomes: training a model, serving inference, settling a transaction, rendering content, running a batch simulation.
Workload Types
Data centers support a wide variety of workloads, each with unique requirements for compute, storage, networking, latency, and compliance. This table summarizes the major workload families covered in this section.
| Workload Type | Focus | Latency Sensitivity | Best-Fit Facilities | Examples |
|---|---|---|---|---|
| AI Training | Massive-scale model training | Medium (hours–days) | AI Factories, Hyperscale | GPT-4/5, LLaMA, Dojo |
| AI Inference | Real-time model serving | High (ms–s) | Hyperscale, Edge, Devices | Chatbots, FSD, Humanoids |
| HPC / Simulation | Scientific and engineering models | Medium–High | Supercomputers, HPC Clusters | Climate, CFD, Genomics |
| Cloud / SaaS | Apps, collaboration, databases | Medium (interactive) | Hyperscale, Colo | Office 365, Salesforce, Zoom |
| Enterprise Apps | ERP, CRM, HRM, regulated IT | Medium | Enterprise DCs, Hybrid IT | SAP, Oracle, Workday |
| Content Delivery | Video, CDN, gaming | High (sub-50 ms for gaming) | Colo, Edge, Hyperscale | Netflix, YouTube, Cloudflare |
| 5G / MEC | Edge compute for 5G apps | Very High (<20 ms) | Edge DCs, Tower Sites | AR/VR, V2X, Private 5G |
| Networking / Telco | Core telco functions, NFV, IXPs | High (carrier-grade) | Telco DCs, IXPs, Colo | AT&T Network Cloud, DT Telco Cloud |
| Government & Defense | Air-gapped, classified, sovereign | High (mission-critical) | Gov DCs, SCIFs, HPC Labs | JWCC, NSA GovCloud, DoE Labs |
| Financial Services | Trading, risk, fraud detection | Ultra High (sub-ms trading) | Colo (exchange-adjacent), Enterprise | NASDAQ, VisaNet, Athena |
| Big Data & Analytics | ETL, data lakes, warehouses | Low–Medium | Hyperscale, Enterprise, Hybrid | Snowflake, BigQuery, Databricks |
| Storage & Archival | Cold storage, compliance archives | Low (hours retrieval OK) | Cold Halls, Tape Libraries, Cloud | AWS Glacier, Iron Mountain, Meta Cold Storage |
Core Sizing & SLO Heuristics
| Workload | Latency Target | Throughput Pattern | Resilience | Growth Pattern |
|---|---|---|---|---|
| AI Training | Fabric µs, job hrs–weeks | Burst batch ? checkpoint | N+1 racks, job-level retry | Stepwise (GPU cohorts) |
| AI Inference | < 10–50 ms | Spiky, 24/7 | Active/active, anycast | Horizontal, per POP |
| HPC | Fabric µs, job hrs–days | Batch queues | Scheduler restarts | Cyclic by project |
| Cloud / SaaS | p50 < 100 ms | Elastic microservices | Multi-AZ/region | Continuous |
| Enterprise | App-dependent | Steady + batch | 2N / N+1 facility | Moderate |
| CDN / Streaming | < 20–80 ms | Peak diurnal/events | Geo-redundant | PoP expansion |
| 5G / MEC | < 5–20 ms | Real-time | Fleet failover | Site replication |
Infrastructure Implications
- Compute: CPUs for SaaS/enterprise; GPUs/ASICs for AI & some HPC; NUMA/memory for DBs.
- Networking: InfiniBand/RoCE for training/HPC; high-fan-out Ethernet for cloud/CDN; 5G backhaul at edge.
- Storage: Parallel FS (Lustre/GPFS/BeeGFS) for HPC; object + NVMe for AI/cloud; caches for CDN/edge.
- Cooling: Liquid (D2C/immersion) for AI/HPC; air + rear-door HX for mixed; ruggedized for edge/micro.
- Energy: Large, steady baseload for AI/HPC; microgrid/BESS for resilience; DER for carbon targets.
- Security/Compliance: Sector-specific (HIPAA, PCI, FedRAMP); zero-trust, HSM/TPM for edge.
Deployment Model Fit
- AI Training: Build-to-suit or modular GPU halls on greenfield campuses.
- Inference/CDN: Colo + metro-edge, container/micro where needed.
- HPC: Government/consortia sites; some enterprise HPC in colo.
- Enterprise Apps: Hybrid IT (on-prem + cloud) via colo interconnects.
- 5G/MEC: Edge fleets, tower sites, private 5G on campuses.
KPIs & Sustainability
- Performance: p50/p95 latency, throughput (QPS/Gbps), job time-to-solution.
- Reliability: SLO/SLI, availability (nines), failover MTTR.
- Efficiency: PUE, WUE, rack density (kW/rack), utilization.
- Carbon: CFE %, Scope 1/2/3 attribution by workload, carbon-aware scheduling.
FAQ
- Can one facility serve multiple workloads? Yes; hyperscalers/colos often host mixed profiles with zoning and tailored power/cooling.
- Where does inference run? Centrally (hyperscale) for scale; at edge/micro for latency and data locality.
- How do workloads affect energy? AI/HPC drive liquid cooling and microgrids; SaaS/enterprise emphasize efficiency and reliability.
- What about data gravity? Large datasets (training, HPC) anchor compute near storage; CDN moves data closer to users.