When AI models transition from being experimental to becoming everyday work tools, a very practical problem arises: how to intelligently distribute the computational load? This is especially true when many servers are located in different regions, and requests to the model fluctuate from nearly nonexistent to sudden spikes.
This is precisely the challenge addressed by the priority-based elastic scheduling mechanism implemented in ACK One Fleet – an Alibaba Cloud tool for managing multiple computing clusters simultaneously.
Difference Between AI Training and Inference
Inference Is Not Training
Before diving into scheduling, it's worth clarifying a term. Inference is the process where a pre-trained model responds to real user queries. Simply put, if training is when the AI learns, then inference is when it works.
Inference requires significant computational resources, especially for large language models. The load on the system is rarely uniform: there can be tens of times more requests during the day than at night. Keeping all possible servers running constantly is expensive. Conversely, turning off spare capacity and failing to handle peaks is detrimental to users.
Challenges of Managing AI Inference in Multi Cluster Environments
One Cluster Is Simple. Multiple Clusters Are More Interesting
If a company has a single data center in one region, managing resources is relatively simple. But large services often operate in hybrid or multi-cluster environments – that is, across several independent groups of servers that may be located in different geographical regions or even in different cloud infrastructures.
In such conditions, questions arise: which cluster should handle a request? How should the load be redistributed if one cluster is overloaded? How can you avoid overpaying for idle resources?
ACK One Fleet offers an approach called priority-based elastic scheduling. In short, the system can automatically direct traffic to where it is most advantageous at the moment, based on predefined priorities.
Priority Based Elastic Scheduling for AI Workloads
Priorities Are Everything
The mechanism is based on a simple idea: not all clusters are equal. Some are “native,” with predictable costs and high reliability. Others are auxiliary – perhaps cheaper, but less preferable under normal conditions.
The system allows you to define the order in which to use clusters. For example:
- first, the primary cluster in its own region;
- if that's full, a backup cluster is engaged;
- if the backup is also overloaded, the load is shifted to third-party resources, including spot instances (temporary computing capacity that cloud providers sell at a lower price but without a guarantee of constant availability).
When the load decreases, the system “scales down” the use of auxiliary resources in reverse order. This is elasticity – the ability to adapt to the current load without requiring manual intervention.
Benefits of Automatic Load Redistribution for AI Services
What This Means in Practice
Imagine a service that provides access to a language model. At night, there are minimal requests, and most servers are idle. In the morning, the load spikes, and additional resources need to be connected quickly. Ideally, this should happen automatically, without operator intervention and without unnecessary costs.
Priority-based elastic scheduling solves exactly this problem. The system itself monitors the status of the clusters, makes decisions about redistributing the load, and does so according to predefined rules – not chaotically, but in line with the priority logic.
Another important point is service stability. In multi-cluster environments, it's easy to encounter a situation where some requests “hang” due to an overloaded node. The scheduling system takes this into account and strives to maintain predictable response times even under peak load.
Hybrid Environments: When the Cloud Mixes with On-Premise Infrastructure
A separate scenario involves hybrid configurations, where some capacity operates in a company's own data center and some in a public cloud. Here, the scheduler's task becomes more complex: it must consider not only the load but also the cost of data transfer between different parts of the infrastructure, latency, and data mobility restrictions.
ACK One Fleet is positioned as a tool that can operate effectively in precisely these conditions – managing resources uniformly, even if everything is structured differently under the hood.
Ideal Use Cases for Multi Cluster Inference Scheduling
Who Needs This and Why
The described approach is primarily relevant for companies that have already deployed AI services in a production environment and are facing real operational challenges. This isn't about research and experiments – it's about making model performance stable, predictable, and cost-effective.
For small teams or early-stage projects, this is likely overkill. But for organizations that handle thousands or millions of requests per day and want to control infrastructure costs, such mechanisms become not an option, but a necessity.
It's also interesting that the approach is not tied to a specific model type. It doesn't matter what is running – a language model, an image recognition system, or something else. The scheduler operates at the infrastructure level, without delving into the content of the requests.
Limitations and Technical Considerations of Elastic Scheduling
Open Questions
Like any infrastructure solution, priority-based elastic scheduling has its limitations and open questions.
First, priority rules must be configured manually – and correctly. A poor configuration could cause the system to switch between clusters too aggressively or, conversely, to be too conservative in keeping the load on an overloaded node.
Second, the mechanism's effectiveness largely depends on how well the monitoring of the clusters themselves is established. If load metrics arrive with a delay or are inaccurate, the scheduler will make decisions based on outdated data.
Third, in multi-regional scenarios, there is always the issue of latency: if a request is sent to a cluster in another region, the user may notice a slowdown. How critical this is for a specific service depends on its nature.
None of this makes the approach non-viable – rather, it serves as a reminder that while automated scheduling offloads some of the operational burden, it doesn't eliminate the need to think about the architecture in advance.