K8s Sidecar Proxy Pod Segmentation
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Solution Overview
Problem
In Kubernetes (K8S), deploying multiple independent models in the same pod limits flexibility and resource utilization, requiring the entire pod to be scaled and recompiled when models are modified, leading to inefficiencies and bottlenecks.
Innovation Solution
Deploying services on a K8S-based method using a sidecar proxy that facilitates communication between pods through a ConfigMap with routing information, allowing for independent scaling and modification of services without recompiling the entire pod.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple independent models are deployed in the same pod, then resource utilization is improved, but flexibility in scaling and modifying individual models deteriorates
Solution Approach 1:
The patent segments the monolithic pod into multiple independent service units, where each service can be scaled and modified independently. By introducing service grids and sidecar proxies, the system enables granular control over individual services while maintaining resource efficiency through shared pod infrastructure.
Solution Approach 2:
The patent introduces sidecar proxies as intermediary components that facilitate communication between services and the service mesh. These proxies enable independent service management, routing, and scaling without requiring changes to the entire pod, thus resolving the contradiction between resource utilization and scaling flexibility.
2Adaptability or versatility
If an independent service is developed to invoke independent models in sequence, then service functionality is improved, but workload in service development increases
Solution Approach 1:
The patent creates a universal service mesh infrastructure that provides common functionality (routing, load balancing, service invocation) across all services. This multi-functional framework reduces the need to develop custom service orchestration logic for each individual service, thereby decreasing development workload while maintaining full service functionality.
Solution Approach 2:
The service mesh enables services to self-configure and self-manage through automated service grid definitions and sidecar proxy deployments. This self-service capability reduces manual service development and configuration workload while maintaining complex service invocation patterns.
3Reliability
If the entire pod is recompiled when an independent model is modified, then model updates are achieved, but time required for modification increases
Solution Approach 1:
The patent segments the pod into independent service units with individual deployment configurations. When a model needs updating, only the specific service containing that model is recompiled and redeployed, not the entire pod. This segmentation enables granular updates that significantly reduce modification time while maintaining model update capability.
Solution Approach 2:
The service mesh introduces dynamic service registration and discovery mechanisms that allow individual services to be updated, registered, and deactivated without affecting the entire pod. This dynamic approach enables continuous deployment of individual models while the rest of the system remains operational, reducing downtime and modification time.
4Quantity of substance
If a pod is deployed with multiple independent models, then resource occupation is improved, but resource utilization efficiency deteriorates due to bottlenecks
Solution Approach 1:
The patent applies local quality by allowing different services within the same pod to have different resource allocations, scaling factors, and performance characteristics. Each service can be optimized independently based on its specific resource consumption patterns, eliminating the bottleneck effect where the most resource-consuming model limits the entire pod's efficiency.
Solution Approach 2:
The service mesh enables dynamic resource allocation and scaling for individual services based on real-time load and performance metrics. This dynamic adjustment allows the system to optimize resource utilization efficiency by allocating more resources to services that need them while maintaining overall resource occupation benefits.
Data Source
AI summary
A K8S-based service deployment method, apparatus, device, and medium, including: acquiring service data, the service data includes data corresponding to one service; deploying the service on a first target pod according to the service data; deploying a proxy corresponding to the service on the first target pod based on a sidecar manner, so as to use the proxy as a network proxy of the service; and acquiring a preset ConfigMap corresponding to the first target pod, whereby the proxy performs communication between different pods according to the preset ConfigMap, the preset ConfigMap includes routing information. As such, only one service is deployed on one pod, and a separate service does not need to be deployed to string together each independent service on one pod, thus reducing the workload in a service deployment process, and improving resource utilization.