The power capping operator seamlessly integrates with existing Kubernetes tools and frameworks to provide a comprehensive solution for power capping and workload optimization in LLM inference services. This document highlights the key integrations and how they enhance the functionality of the power capping operator.
The power capping operator integrates with KEDA to enable event-driven autoscaling of LLM inference service deployments based on the specified power cap and workload requirements.
graph LR
A[Power Capping Operator] --> B[KEDA]
B --> C{LLM Inference Service}
Integration with KEDA provides the following benefits:
- Automatically scales the LLM inference service deployment based on the power consumption and workload demands
- Ensures that the power cap limit is not exceeded while maintaining the desired performance levels
- Optimizes resource utilization by scaling the deployment up or down based on the specified policies
To enable KEDA integration, create a ScaledObject resource that defines the scaling rules and targets for the LLM
inference service deployment. The power capping operator will automatically configure KEDA based on the power capping
policies defined in the CRD.
The power capping operator integrates with KServe, a Kubernetes-native platform for serving machine learning models, to manage the serving of LLM inference workloads and optimize their placement and scaling.
graph LR
A[Power Capping Operator] --> B[KServe]
B --> C{LLM Inference Service}
Integration with KServe provides the following benefits:
- Seamlessly manages the deployment and scaling of LLM inference services using KServe's serving capabilities
- Optimizes workload placement and resource allocation based on the power capping policies and rack-level constraints
- Leverages KServe's built-in features such as model versioning, canary deployments, and traffic splitting
To enable KServe integration, create an InferenceService resource that defines the LLM model and serving
configuration. The power capping operator will work in conjunction with KServe to manage the power consumption and
workload placement of the inference service.
The power capping operator integrates with Kepler, a power monitoring tool for Kubernetes, to collect power consumption data from the CPU and GPU resources on the servers.
graph LR
A[Power Capping Operator] --> B[Kepler]
B --> C{Node Resources}
Integration with Kepler provides the following benefits:
- Accurate and real-time power consumption data for CPU and GPU resources
- Visibility into the power usage patterns of LLM inference workloads
- Enables the power capping operator to make informed decisions about workload placement and power capping based on the collected data
To enable Kepler integration, deploy Kepler on your Kubernetes cluster and configure it to monitor the desired node resources. The power capping operator will automatically discover and use the power consumption data provided by Kepler.
The power capping operator can integrate with custom metric adapters to collect power consumption data from various sources and use them for power capping and workload optimization.
graph LR
A[Power Capping Operator] --> B[Custom Metric Adapter]
B --> C{External Monitoring System}
Integration with custom metric adapters provides the following benefits:
- Flexibility to collect power consumption data from external monitoring systems or custom data sources
- Enables the power capping operator to work with existing monitoring infrastructure and tools
- Allows for the use of specialized power monitoring devices or APIs
To integrate with a custom metric adapter, implement the required interface and configure the power capping operator to use the adapter for collecting power consumption data. The adapter should retrieve the power consumption metrics from the desired source and provide them to the power capping operator in a compatible format.
The power capping operator integrates with monitoring and alerting systems to provide visibility into the power consumption and workload performance of LLM inference services.
graph LR
A[Power Capping Operator] --> B[Monitoring System]
A --> C[Alerting System]
Integration with monitoring and alerting systems provides the following benefits:
- Real-time monitoring of power consumption and workload performance metrics
- Alerting capabilities to notify operators when power cap limits are exceeded or performance degradation occurs
- Dashboards and visualizations to gain insights into the power usage and workload behavior
To enable monitoring and alerting integration, configure the power capping operator to export relevant metrics to the desired monitoring system (e.g., Prometheus) and set up alerting rules based on the defined thresholds and policies. The monitoring system can then be used to visualize the metrics and trigger alerts when necessary.
These integrations enhance the power capping operator's functionality and provide a comprehensive solution for managing power consumption and workload optimization in LLM inference services running on Kubernetes.