1.8. Multi-Tenancy and Resource Management
🪄 Step 1: Intuition & Motivation
Imagine you’re running a food court 🍜🍕🍣 — dozens of restaurants, one shared kitchen.
Each restaurant (team or product) needs its own oven (model server), utensils (GPUs/CPUs), and staff (threads or containers). But if everyone just grabs what they want, chaos erupts — ovens overheat, queues pile up, and food arrives late.
That’s exactly what happens in multi-model ML serving environments.
Modern organizations don’t serve one model; they serve hundreds — fraud detection, recommendations, search, ads, personalization — all competing for limited hardware.
So how do we keep everyone happy, efficient, and fair? We need multi-tenancy — designing a platform where many models coexist peacefully — and resource management — ensuring everyone gets their fair share of compute pie 🥧.
🌱 Step 2: Core Concept
Multi-tenancy is about sharing infrastructure without sacrificing performance, isolation, or fairness. Let’s break down how that works in ML systems.
🏢 What is Multi-Tenancy in ML?
Definition: A multi-tenant ML system serves multiple models or workloads on the same infrastructure, often with different owners, SLAs (Service Level Agreements), and priorities.
In simple terms:
Many models. Shared servers. Predictable performance.
Examples:
- A retail company hosts separate models for pricing, recommendations, and demand forecasting.
- A platform like Uber runs hundreds of models for ETA prediction, surge pricing, fraud detection, etc.
The challenge? Balancing efficiency (resource reuse) with isolation (no interference).
⚙️ Model Serving Platforms – The Infrastructure Layer
Multi-tenancy starts with a serving platform — a centralized system that hosts and manages models.
Popular examples:
- TensorFlow Serving — optimized for TensorFlow models
- Triton Inference Server (NVIDIA) — supports TensorFlow, PyTorch, ONNX, and custom backends
- Ray Serve — flexible, Pythonic system for distributed model serving
Responsibilities of the serving platform:
- Routing: Send each request to the correct model version.
- Scaling: Adjust replicas dynamically based on traffic.
- Concurrency: Handle many requests simultaneously.
- Isolation: Prevent one model from degrading others.
🔁 Autoscaling & Resource Allocation – The Balancing Act
Not all models are equal — some need GPUs, others can survive on CPUs. Some need 10 ms latency, others can tolerate a few seconds.
To balance this diversity, ML systems use autoscaling and resource allocation policies:
⚙️ Autoscaling
- Horizontal Scaling: Add/remove instances based on load (e.g., number of inference requests).
- Vertical Scaling: Adjust resource power (e.g., assign bigger GPU when demand spikes).
- Predictive Scaling: Use past traffic patterns to anticipate future spikes.
⚡ Resource Partitioning
Assign fixed resource quotas per model/team:
- Model A → 2 GPUs
- Model B → 8 CPUs
- Model C → shared pool
This avoids a “noisy neighbor” — when one model hogs compute and others starve.
🧩 Load Balancing
Distribute incoming requests evenly across replicas using policies like:
- Round Robin: Simple equal distribution.
- Least Loaded: Prioritize free instances.
- Latency-Aware: Route to the server responding fastest.
🧮 Dynamic Batching – Speed Through Grouping
Running one prediction at a time is inefficient — GPUs love parallel work.
Dynamic batching collects multiple inference requests into a mini-batch and runs them together, drastically improving throughput.
Example: If 10 users request predictions at the same time, instead of 10 single calls, the server batches them as one tensor through the GPU.
Benefits:
- Higher hardware utilization
- Lower average latency (due to parallel execution)
But batching too much introduces delays. So there’s a trade-off between latency (wait for batch) and efficiency (GPU throughput).
📦 Kubernetes and Container Orchestration
Kubernetes (K8s) acts as the manager of all these serving instances.
It handles:
- Pod Scheduling: Decides which machine runs which model.
- Resource Quotas: Enforces CPU/GPU limits per container.
- Health Probes: Automatically restarts failed pods.
- Horizontal Pod Autoscaler (HPA): Scales based on CPU/GPU load.
In large organizations, Kubernetes + a model-serving layer (like Triton) = full multi-tenant infrastructure backbone.
📐 Step 3: Mathematical Intuition (Conceptual)
Resource allocation can be framed as an optimization problem:
$$ \text{Maximize} \quad \sum_i U_i(R_i) $$Subject to:
$$ \sum_i R_i \leq R_{total} $$Where:
- $U_i(R_i)$ = utility (performance) of model $i$ given resources $R_i$
- $R_{total}$ = total available compute resources
The platform’s job is to maximize total utility (system performance) while respecting limited resources.
🧠 Step 4: Key Design Question — Latency-Aware Serving
“How would you design a platform that serves 100 models, each with a different latency SLA?”
Answer conceptually:
- Tag each model with a latency target (e.g., <10ms, <100ms, <500ms).
- Prioritize resource allocation — assign GPUs to low-latency models, CPUs to high-latency ones.
- Use queue prioritization — shorter queues for time-sensitive requests.
- Isolate high-SLA models — prevent noisy neighbor interference.
- Dynamic scaling — expand replicas when latency approaches threshold.
In essence: treat models not equally, but fairly, based on business importance.
⚖️ Step 5: Strengths, Limitations & Trade-offs
- Efficient use of hardware across teams.
- Scalable and flexible — easy to add new models.
- Built-in fault tolerance through container orchestration.
- Complex to configure (Kubernetes, GPU sharing, scaling policies).
- Debugging cross-tenant latency issues can be hard.
- Requires strong governance to prevent resource abuse.
Trade-off between isolation and utilization:
- More isolation → safer but less efficient.
- More sharing → efficient but risk of interference. Balance depends on the organization’s tolerance for performance variance.
🚧 Step 6: Common Misunderstandings
🚨 Common Misunderstandings (Click to Expand)
- “Each model needs its own server.” → Wrong. Shared serving platforms are the norm.
- “Autoscaling always saves cost.” → Only if configured correctly; over-scaling can waste resources.
- “GPU sharing slows everything.” → Not necessarily; batching and scheduling make sharing efficient.
🧩 Step 7: Mini Summary
🧠 What You Learned: How large-scale ML platforms serve many models simultaneously while maintaining fairness and speed.
⚙️ How It Works: Through serving platforms (like Triton), autoscaling, load balancing, and dynamic batching managed by Kubernetes.
🎯 Why It Matters: Efficient resource sharing turns chaos into harmony — enabling ML at enterprise scale without burning GPU farms.