Cloud Traffic Routing Across Serverless and Container Frameworks
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Solution Overview
Problem
Switching between serverless and container-based frameworks in a cloud computing environment for tasks is inefficient due to the need for separate codebases, consuming resources and time, and existing load balancers cannot determine the most efficient framework based on metrics.
Innovation Solution
A system that monitors traffic metrics and uses a machine learning model to dynamically route traffic to either a serverless or container-based framework, deploying a single codebase that is compatible with both frameworks, and automatically scales down unused resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate codebases are used for serverless and container-based frameworks, then each framework can be optimized independently, but resource consumption and deployment time increase
Solution Approach 1:
The patent implements a universal codebase that can be deployed across both serverless and container-based frameworks through abstraction layers and adapter patterns. The system detects the target framework at runtime and adapts the single codebase accordingly, eliminating the need for separate codebases while maintaining framework-specific optimizations through configuration rather than structural duplication.
2Reliability
If separate codebases are maintained for different frameworks, then framework-specific requirements are met, but deployment time and maintenance effort increase
Solution Approach 1:
A single universal codebase is designed to work across multiple frameworks by implementing framework-agnostic core logic with adapter layers for framework-specific operations. This reduces deployment time by eliminating the need to maintain and synchronize multiple codebases while ensuring framework compatibility through standardized interfaces.
Solution Approach 2:
The system dynamically detects the target framework at runtime and adjusts its behavior accordingly, allowing the same codebase to adapt to different framework environments without requiring separate compiled versions or manual reconfiguration, thereby accelerating the deployment process.
3Ease of operation
If load balancers use simple routing rules, then implementation is straightforward, but they cannot determine the most efficient framework based on metrics
Solution Approach 1:
The load balancer implements feedback-based routing by continuously monitoring performance metrics from both serverless and container-based frameworks and using this information to dynamically determine the most efficient routing decision. The system learns from historical performance data and adjusts routing rules to optimize resource utilization while maintaining straightforward operation through automated decision-making.
Solution Approach 2:
The routing rules transition from static to dynamic, allowing the load balancer to adapt routing decisions based on real-time metrics such as latency, throughput, and resource availability. This enables the system to automatically route traffic to the most efficient framework without requiring complex manual configuration.
Data Source
AI summary
In some implementations, a device may obtain a codebase, associated with an application and one or more services of the application, that is deployable via both a first framework of the cloud computing environment and a second framework of the cloud computing environment. The device may receive the traffic associated with the application. The device may monitor one or more metrics associated with the traffic. The device may deploy, based on the one or more metrics, the codebase via the first framework or the second framework in the cloud computing environment. The device may route, based on the one or more metrics, the traffic to the first framework or to the second framework.