Cloud Policy Reroute for Safe Service Deployment

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The deployment of modifications, such as updates and upgrades, to cloud-based services is cumbersome, resource-intensive, and prone to unsafe practices, leading to inefficient operation and potential disruptions.

Innovation Solution

The implementation of policy rerouting through tenant-defined config files allows for safe deployment of modifications by gradually increasing deployment across nodes and enabling A/B testing, allowing for dynamic execution flow adjustments based on user attributes and conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If modifications are deployed to cloud-based services, then service functionality is improved, but resource consumption increases and disruptions occur

Engineering Contradiction:

Improveservice functionalityVSAvoidresource consumption

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The deployment process is segmented into multiple phases: initial deployment to a subset of nodes, validation phase, and gradual rollout to remaining nodes. This segmentation allows functionality improvement while controlling resource consumption by limiting simultaneous deployment operations.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts deployment parameters including the rate of node inclusion, resource allocation, and rollback thresholds based on real-time monitoring of service performance and resource utilization. This dynamic approach optimizes the balance between functionality improvement and resource consumption.

Inventive Principle:

Principle #15Dynamics

2Speed

If modifications are deployed across all nodes simultaneously, then deployment speed is improved, but system reliability deteriorates due to potential errors affecting all nodes

Engineering Contradiction:

Improvedeployment speedVSAvoidsystem reliability

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The node population is segmented into deployment batches with controlled sizes. Each batch is deployed and validated before proceeding to the next batch, maintaining system reliability while achieving reasonable deployment speed through parallel processing of batches.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements pre-deployment validation, rollback mechanisms, and error buffering before full deployment. These cushioning measures prepare the system to handle potential errors without compromising overall reliability, allowing faster deployment across segmented node groups.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

3Adaptability or versatility

If code modification requests are processed by the cloud-based service provider, then service adaptability is improved, but deployment complexity and resource intensity increase

Engineering Contradiction:

Improveservice adaptabilityVSAvoiddeployment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements self-service deployment automation including automatic code validation, incremental deployment execution, and self-healing rollback capabilities. This automation reduces deployment complexity while maintaining service adaptability by eliminating manual intervention in the deployment process.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The deployment system manages complexity by parameterizing deployment configurations including node selection criteria, deployment rate limits, and validation thresholds. These configurable parameters allow the system to adapt to different service requirements without increasing inherent deployment complexity.

Inventive Principle:

Principle #35Parameter changes

4Ease of operation

If tenant-specific code modifications are implemented, then ease of operation for tenants is improved, but system complexity and resource consumption increase

Engineering Contradiction:

Improveease of operationVSAvoidresource consumption

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The system uses template-based code modification where tenant-specific customizations are created as copies or variations of base templates. This copying approach enables easy tenant operation with customized code while reducing resource consumption by avoiding redundant full code deployments for each tenant.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

Code modifications are applied locally to specific tenant instances rather than system-wide. This local quality approach allows each tenant to have customized code while minimizing overall resource consumption by only deploying and processing modifications where needed.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11303523B2Cloud-based service policy reroute

Publication Date: 2022.04.12 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions. The instructions may cause the processor to receive a call request for a cloud-based service and retrieve a first policy corresponding to the cloud-based service, the first policy identifying a policy reroute. The instructions may also cause the processor to determine which second policy of a plurality of second policies that corresponds to the policy reroute and retrieve the second policy that the corresponds to the policy reroute. The instructions may further cause the processor to apply the second policy on the cloud-based service and respond to the call request following the application of the second policy.