Breaking the Monolith: Modular Multi-Tiered Load Balancing in Serverless Computing

In the evolving landscape of cloud computing, serverless architectures promise effortless scalability and reduced operational overhead. But beneath the abstraction lies a real challenge: efficiently distributing work across a heterogeneous, distributed runtime without creating bottlenecks or single points of failure.

In my final-year dissertation—later incorporated into the SoCC'24 paper on dynamic multi-tiered load balancing—I tackled this challenge by redesigning the load balancing system for AutoNDP, a funclet-migration runtime built on Faasm.

The Problem

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The original AutoNDP runtime relied on:

• A single compute node as the entry point for all invocations.
• Static round-robin balancing between compute nodes.
• Minimal configurability and no real adaptation to workload changes.

This design created clear scaling limits:

• One node acted as a bottleneck and single point of failure.
• The scheduler only redistributed work after a node hit capacity.
• No flexibility for cloud providers to tune or replace policies.

My Contribution: Multi-Tiered, Modular Load Balancing

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I implemented a three-layer balancing framework designed around three principles: Configurability, Modularity, and Scalability.

1. Invocation Layer Distribution

At the system's entry point, I introduced a configurable dispatcher:

• Replaced the single-node invoker with a round-robin dispatcher across all compute nodes.
• Supported pluggable policies—administrators can select or implement their own by conforming to an ILoadBalancerStrategy interface.
• State persisted across invocations using Redis, enabling distribution decisions without losing context.

2. Inter-Node Balancing

I refactored Faasm’s tightly coupled scheduler into a policy-driven engine:

• Decoupled scheduling logic from core runtime to support modular policies like MostSlots and LeastLoad.
• Integrated a metrics collection module to provide live CPU, RAM, and load average data from each node via /proc.
• Allowed policies to make real-time, informed balancing decisions.

3. Storage Layer Data Distribution

At the Ceph storage layer:

• Increased placement groups from 1 → 128 for better parallelism.
• Enabled autoscaling and rebalanced data to prevent hot-spotting.
• Extended offloading decisions beyond simple thread availability to include CPU, memory, and load thresholds—fully configurable per deployment.

Why Modularity Matters

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By designing the balancer as a set of interchangeable components, AutoNDP now:

• Supports deployment-specific tuning without modifying core code.
• Allows experimentation with new strategies (including future ML-based policies).
• Can scale horizontally without major re-architecture.

Results

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Benchmarks showed:

• 3.13× lower median latency for I/O-intensive workloads.
• 10× reduction in latency variance under high concurrency.
• More stable throughput with reduced queuing effects.

While some end-to-end workloads showed the storage offloading bottlenecks still need attention, the architectural flexibility I introduced positions the system for future scaling without major rewrites.

Looking Ahead

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The multi-tiered load balancer opens the door to:

• Adaptive policies that evolve with workload patterns.
• Integration with predictive schedulers and demand forecasting.
• Applying the modular approach to other runtime services in distributed FaaS systems.

This work was not just an exercise in squeezing out performance—it was a case study in engineering for flexibility, ensuring that when workloads, hardware, or requirements change, the system can adapt without tearing apart its foundation.