In a k8s cluster, the reasons for node load imbalance may include the following aspects:
1. Uneven node resource balancing: Although the polling strategy will distribute traffic as evenly as possible, if the nodes in the cluster have different processing capabilities (for example, some nodes have higher CPU or memory configurations), it may Will cause load imbalance.
2. Uneven distribution of requests: When traffic is high, requests may be concentrated on certain pods. For example, during student exams, a large number of accesses are on the exam service, so there will be less traffic for homework and live broadcast pods. If all the exam containers are on one node at this time, and the job and live broadcast pods are on another node, it will inevitably lead to a high load on the node where the exam is located, while the load on other nodes is relatively low.
3. Network conditions: Network conditions may affect the distribution of traffic. For example, some nodes may have faster network connections, causing requests to be delivered to those nodes more quickly, resulting in load imbalance.
4. Scheduler issues : Kubernetes’ scheduler may cause load imbalance in some cases. For example, if the scheduler does not take into account the actual resource utilization of the nodes, it may result in some nodes being overloaded and other nodes being underloaded.
In order to solve the problem of node load imbalance, the following measures can be taken:
1. Optimize request processing time: If the request processing time is unevenly distributed, you can try to optimize the request processing time to make the request processing time more even, thereby avoiding load imbalance.
2. Ensure node resource balance: Ensure that all nodes in the cluster have similar resource configurations to avoid load imbalance due to hardware differences.
3. Optimize network configuration: Adjust network configuration to ensure that network connection speeds are evenly distributed among all nodes to avoid load imbalance due to network conditions.
4. At the pod level: Are there scheduling rules set for the pod? For example, we commonly use PodAntiAffinity, which allows unused replica pods of the same business to be dispersed on different servers. This is to ensure load balancing and also to increase the fault-tolerance mechanism.
5. Adjust the scheduler strategy: Adjust the scheduler strategy according to the actual situation to better balance the node load. For example, Kubernetes' scheduler can be tuned to take into account a node's actual resource utilization. In addition to the polling strategy, Kubernetes also provides a variety of other scheduler strategies. Here are some common strategies: Random strategy (Random): Randomly select a node to schedule tasks. Priority Preemptive: Scheduling is performed according to the priority of tasks. High-priority tasks can preempt the resources of low-priority tasks. Fair Share strategy: Scheduling is performed based on the resource allocation of each node to ensure that each node has a fair chance to receive tasks. Constraint Satisfaction: Scheduling based on a series of constraints, such as meeting certain label requirements or location constraints, etc.
Conclusion
Of course, these are only some possible causes and solutions at the k8s level. For external web products such as nginx, we must also choose appropriate strategies to make our cluster more robust. This is my personal understanding and opinion on the problem of "node load imbalance in k8s cluster". I hope it can be helpful to everyone.