Policy-Driven Homing Service System for Cloud Orchestration
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Solution Overview
Problem
Current orchestration systems in service providers face complexity and inefficiency due to over-complicated network status readouts, non-specific manipulations, and the inability to quickly revert changes, leading to frustration among operators.
Innovation Solution
A policy-driven homing service system that receives demands from a master service orchestrator, applies homing constraints to determine optimal solutions for service component placement across multiple sites and clouds, ensuring compliance with service requirements, optimization objectives, and cloud capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If orchestration systems perform automatic network manipulations to reduce manual operations, then operational efficiency is improved, but the complexity of system configuration and the difficulty of reverting changes increase
Solution Approach 1:
The system performs preliminary actions by automatically creating parent-child relationships between network objects and pre-configuring homing constraints before service deployment. This automation of preparatory configuration tasks reduces manual operational complexity while maintaining efficient service delivery, as the system proactively establishes the structural framework needed for automatic manipulations.
Solution Approach 2:
The system implements feedback mechanisms through service level agreement monitoring and performance tracking. When service performance deviates from constraints, the system automatically adjusts configurations and can revert changes if constraints are violated. This closed-loop feedback ensures that automatic manipulations remain controllable and reversible, reducing the perceived complexity for operators.
2Reliability
If orchestration systems provide detailed network status readouts for monitoring, then service quality control is improved, but the complexity of interpreting and managing network status increases
Solution Approach 1:
The system applies local quality by providing differentiated monitoring views tailored to specific service components and their constraints. Rather than presenting a uniform complex readout of all network parameters, the system focuses monitoring details on relevant service level agreement metrics and homing constraint compliance, making the status information more manageable and interpretable for operators while maintaining rigorous quality control.
Solution Approach 2:
The system segments network status monitoring into hierarchical levels: service-level metrics for high-level oversight, component-level metrics for detailed analysis, and constraint-specific metrics for compliance verification. This segmentation allows operators to access detailed information when needed while presenting a simplified overview during normal operations, reducing the perceived complexity of network status management.
3Productivity
If the system automates service component placement across multiple sites and clouds, then deployment efficiency is improved, but the computational complexity of determining optimal placements increases
Solution Approach 1:
The system performs preliminary computational work by pre-establishing homing constraints, parent-child relationships, and service level agreements before deployment decisions are required. By pre-configuring the optimization framework and constraint structures, the system reduces the computational burden during actual deployment operations, allowing efficient automated placement without requiring complex real-time calculations for each service component.
Solution Approach 2:
The system implements dynamic optimization by adjusting placement strategies based on current resource availability, service demands, and constraint compliance. Rather than using static complex algorithms, the system adapts its computational approach to the specific deployment context, using simplified heuristics when appropriate and more sophisticated optimization only when necessary, thereby reducing overall computational complexity while maintaining deployment efficiency.
Data Source
AI summary
The concepts and technologies disclosed herein are directed to a policy-driven homing service system that can receive, from a master service orchestrator, a demand specifying a service component to be used to provide, at least in part, a service. The system can receive, from a policy system, a homing constraint. The system can determine, for the demand, an initial set of all potential candidates. The system can apply the homing constraint to each potential candidate in the initial set of all potential candidates. The system can determine a resultant set of potential solutions that satisfy the homing constraint. The system can determine a best solution from the resultant set of potential solutions. The system can send the best solution to the master service orchestrator, which instantiates the demand based upon the best solution.