Edge Cluster Pod Segmentation for Independent L4 L7 Scaling
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Solution Overview
Problem
Kubernetes, a de-facto orchestration platform, struggles to efficiently manage network function virtualization (NFV) data plane functions due to differences between web applications and NFV, particularly in handling L5-L7 services, which are not well-suited for the current edge node architecture based on single appliance models using DPDK.
Innovation Solution
Deploying an edge device as a cluster of pods, where L4 and L7 services are scaled independently, with L4 pods handling L4 operations and L7 pods handling L5-L7 operations, utilizing SDN managers and controllers for orchestration, and employing horizontal, vertical, and cluster autoscalers for resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a single appliance model with DPDK is used for edge services, then L4 services performance is improved, but L5-L7 services scalability is limited
Solution Approach 1:
The edge device is segmented into multiple independent pods, each capable of handling different service types (L4, L5, L6, L7). This segmentation allows L4 services to be handled by dedicated pods using DPDK for high performance, while L5-L7 services are distributed across multiple pods for improved scalability and flexibility.
Solution Approach 2:
The pod-based architecture creates universal computing units that can be configured to perform multiple service types. Each pod can be dynamically assigned to handle L4, L5, L6, or L7 services based on demand, making the system universally adaptable to different service requirements while maintaining high performance for L4 and enabling scalability for L5-L7.
2Device complexity
If L4 and L7 services are integrated in a single edge appliance, then device complexity is reduced, but independent scaling capability is lost
Solution Approach 1:
The edge appliance is divided into separate L4 pods and L7 pods that can be independently deployed and scaled. This segmentation maintains manageable complexity through modular design while enabling independent scaling of each service type based on specific workload requirements.
Solution Approach 2:
The system implements dynamic scaling capabilities where L4 and L7 pods can be independently scaled based on real-time service demands. The pod orchestrator dynamically manages the deployment, provisioning, and scaling of individual pod types, allowing the system to adapt to changing workload requirements without requiring scaling of the entire edge appliance.
3Adaptability or versatility
If a cluster of pods is deployed for edge services, then service scalability is improved, but system complexity increases
Solution Approach 1:
A pod orchestrator acts as an intermediary between the management system and individual pods, abstracting away the complexity of cluster management. The orchestrator handles pod deployment, provisioning, scaling, and coordination, thereby enabling service scalability while shielding users from the underlying system complexity.
Solution Approach 2:
The pod-based architecture enables self-service capabilities where individual pods can be independently deployed, scaled, and managed without requiring manual intervention for the entire cluster. The orchestrator automatically manages pod lifecycles, allowing services to scale dynamically based on demand while reducing operational complexity through automation.
Data Source
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AI summary
Some embodiments provide a novel method for deploying an edge device as a cluster of pods. The method receives a set of criteria for deploying the edge device. The method uses the set of criteria to deploy the edge device as a set of one or more pods executing on a set of one or more nodes. The method implements, on the set of pods, a set of one or more services to perform on data message flows. At least two pods deployed for the edge cluster perform different service operations of different service types such that the different service types are able to be scaled independently.