AI WASM Load Balancer Multi-Instance Intelligent Routing For Non-Kubernetes Environments

Sep 2, 2025 by ADMIN 89 views

Hey guys! Today, we're diving deep into an exciting feature request for the AI WASM Load Balancer – specifically, how to make it work like a charm in non-Kubernetes environments. We're talking about scenarios where you might have bare metal servers, VMs, or even a mix of different environments. The goal? To enable intelligent routing across multiple instances, even when you're not running on Kubernetes.

The Need for Intelligent Routing in Non-K8s Environments

So, what's the big deal? Well, in non-Kubernetes environments, the existing AI load balancer (with strategies like prefix_cache, global_least_request, and least_busy) can't fully flex its muscles when it only sees a single upstream. It’s like having a super-smart traffic cop who can only direct cars to one destination – not very efficient, right? The main goal of this feature request is to empower the AI WASM Load Balancer to make intelligent routing decisions across multiple instances in these environments.

The Current Limitations

Currently, the AI load balancer's intelligent pod selection capabilities are somewhat limited in non-K8s setups. When the load balancer only sees a single upstream, it misses out on the chance to distribute traffic smartly across multiple instances. This is a significant limitation in environments where you might have several instances of a service running on different machines or VMs. Imagine you have a bunch of powerful servers, but your load balancer can only talk to one at a time – that’s a lot of wasted potential!

The Vision: Static Multi-Instance Support

The idea is to allow users to manually list multiple instances (think static IPs or hostnames) and still leverage those awesome routing strategies like prefix-cache. This would unlock the true potential of the AI load balancer in non-K8s environments. This would allow for more granular control over how traffic is distributed, ensuring that each instance is utilized efficiently.

Goals for the Feature

Let's break down the specific goals we're aiming for with this feature:

G1: Static Instance Configuration: We want to be able to list N real LLM (Large Language Model) instances via static configuration in non-K8s environments. This means you could specify a list of IP addresses or hostnames, and the load balancer would know about each instance.
G2: Preserve Existing Load Balancing Strategies: We want to keep those existing AI load balancer strategies (prefix_cache, global_least_request, least_busy) working perfectly. No need to reinvent the wheel – we just want to extend their reach.
G3: Prefix Cache Instance Stickiness: The prefix_cache strategy should maintain its “instance stickiness” magic, ensuring that requests with the same prefix are consistently routed to the same instance. This is crucial for maintaining session affinity and improving performance. This is particularly important for caching scenarios, where you want to ensure that data is consistently served from the same instance.

How Could It Be? Envisioning the Solution

So, how could we make this happen? Let's dive into the nuts and bolts of what a solution might look like. The core idea is to enhance the AI WASM Load Balancer to recognize and utilize multiple statically defined instances in non-K8s environments. This involves several key aspects, including configuration, routing logic, and health checks.

Configuration

The first step is to provide a way for users to specify the list of instances. This could involve a configuration file (like YAML or JSON) where users can list the IP addresses, hostnames, or other identifiers for each instance. The configuration should also allow for specifying metadata, such as GPU type, zone, or weight, which can be used for more advanced routing decisions.

For example, a configuration might look like this:

instances:
  - address: 192.168.1.10
    metadata:
      gpu: nvidia-tesla-v100
      zone: us-east-1
      weight: 80
  - address: 192.168.1.11
    metadata:
      gpu: nvidia-tesla-p100
      zone: us-east-1
      weight: 20

This configuration lists two instances with different IP addresses and metadata. The metadata can then be used to influence routing decisions, such as sending more traffic to instances with better GPUs or instances in a specific zone.

Routing Logic

With the instances defined, the next step is to enhance the routing logic to utilize this information. This means modifying the load balancing strategies (prefix_cache, global_least_request, least_busy) to consider the available instances and their metadata.For example, the prefix_cache strategy would need to maintain a mapping of prefixes to instances, ensuring that requests with the same prefix are consistently routed to the same instance. The global_least_request strategy would need to track the number of active requests for each instance and route new requests to the least loaded instance. The least_busy strategy would need to take into account not only the number of requests but also the resource utilization (e.g., CPU, memory) of each instance.

Health Checks

Health checks are crucial for ensuring that traffic is only routed to healthy instances. In a non-K8s environment, this means implementing a mechanism to monitor the health of each instance and remove unhealthy instances from the routing pool.This could involve periodic probes to each instance, checking for things like HTTP status codes, response times, or custom health indicators. If an instance is deemed unhealthy, it should be automatically removed from the routing pool until it recovers.This health-checking mechanism should also be configurable, allowing users to define the frequency and type of probes, as well as the criteria for determining health.

Potential Challenges and Considerations

Of course, implementing this feature isn't all smooth sailing. There are a few potential challenges and considerations we need to address.

1. Host Enumeration

Currently, the system might be limited to polling only one upstream address. We need to make sure it can handle multiple instances.

The current implementation might assume a single upstream host, which limits its ability to distribute traffic across multiple instances. This needs to be addressed by allowing the load balancer to enumerate and monitor multiple upstream hosts.

2. Override Risks

The SetUpstreamOverrideHost might only work for existing hosts, which could be a problem if we're dynamically adding or removing instances. This function is crucial for overriding the upstream host in certain scenarios, but its limitation to existing hosts could pose a challenge when dealing with dynamic instance lists.

3. Metadata Limitations

We need to figure out how to handle metadata (like GPU type, zone, or weight) for instances. This metadata is essential for making informed routing decisions, such as prioritizing instances with better hardware or instances in a specific region. The current implementation might lack the ability to filter or sort instances based on metadata, which would limit its flexibility.

4. Health Checks and Failure Handling

Robust health checks are crucial. We need a way to detect and remove unhealthy instances so we don't send traffic to them. Without proper health checks, the cache stickiness could actually amplify failures by consistently routing traffic to a broken instance. This is a critical aspect of ensuring high availability and reliability.

5. Lack of Documentation

Finally, we need clear documentation for non-K8s standard practices. Users shouldn't have to摸索（mōsuǒ - grope; feel about; fumble）their way through this – we want to make it easy to use!The absence of clear documentation for non-K8s deployments increases the learning curve for users and can lead to misconfigurations. Providing comprehensive documentation, including best practices and examples, is essential for ensuring user success.

In Conclusion: Leveling Up AI WASM Load Balancing

All in all, adding multi-instance support for non-K8s environments is a huge step forward for the AI WASM Load Balancer. It's about unlocking its full potential and making it a versatile tool for all sorts of deployments. By addressing the challenges and focusing on a clear, user-friendly approach, we can make this a killer feature. We need to solve existing problems to ensure high availability. This will unlock the true power of AI WASM Load Balancer in diverse environments. Let's make it happen, guys!