API Multiplexing for Batched Pod Planning and Deployment
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Solution Overview
Problem
Cloud-network architectures face inefficiencies in handling multiple pod planning requests simultaneously, leading to significant wait times and resource underutilization due to serial processing, which is exacerbated at scale.
Innovation Solution
A system that batches pod planning requests and processes them collectively, reducing the need for individual resource determination and deployment time by handling multiple pods as a group, thereby optimizing resource allocation and deployment efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pod planning requests are processed serially one at a time, then resource allocation accuracy is maintained, but deployment time increases significantly
Solution Approach 1:
The patent segments the pod planning process into two distinct phases: a shared planning phase where multiple pod requests are processed collectively to determine resource requirements, and an individual deployment phase where each pod is deployed to a specific host. This segmentation allows the system to benefit from batch processing efficiency while maintaining the accuracy needed for individual resource allocation.
Solution Approach 2:
The patent merges multiple pod planning requests into a single batch processing operation. Instead of handling each request separately, the system combines multiple requests and processes them together to determine resource requirements, thereby reducing the total planning time while maintaining accurate resource allocation through the subsequent individual deployment phase.
2Productivity
If multiple pod requests are batched together, then deployment speed improves, but resource allocation complexity increases
Solution Approach 1:
The patent segments the planning process into a shared planning phase that handles multiple requests collectively and an individual deployment phase for each pod. This segmentation reduces complexity by separating the batch processing operations from the individual resource allocation decisions, making the system more manageable despite handling multiple requests.
3Measurement precision
If serial processing is used for each pod request, then resource requirements are accurately determined, but system wait time increases
Solution Approach 1:
The patent segments the processing into a shared planning phase where resource requirements are determined collectively for multiple pods, and an individual deployment phase where accurate resource allocation is ensured. This segmentation maintains measurement precision for resource requirements while significantly reducing system wait time through batch processing.
Solution Approach 2:
The patent performs preliminary resource requirement determination in the shared planning phase for all pods in the batch before individual deployment. This preliminary action establishes accurate resource requirements upfront, allowing subsequent individual deployments to proceed quickly without re-evaluating resource needs, thus reducing overall system wait time while maintaining accuracy.
Data Source
AI summary
A method for organizing and deploying containerized applications within a cloud-network architecture framework. The steps include receiving a plurality of pod requests. The steps include organizing the plurality of pod requests into one or more batches. The steps include, for each of the one or more batches, determining a resource requirement for each pod request in the plurality of pod requests in the batch. The steps further include determining a host availability and a host resource availability of one or more hosts. The steps further include deploying each pod request in the plurality of pod requests in each of the one or more batches to the one of the one or more hosts based on the host availability and the host resource availability.