LCS Policy Scheduler for Dynamic Workload Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing policy management systems face challenges with peak workloads and resource contention due to overlapping policy executions, leading to performance degradation and potential starvation, especially in dynamic environments with multiple vendors and custom policies.
Innovation Solution
A Learning Classifier System (LCS) scheduler that adaptively tunes policy schedules based on real-time system workload, using reinforcement learning and evolutionary algorithms to evolve rules and minimize CPU and memory spikes, ensuring efficient resource utilization and conflict-free policy execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple policies are executed in parallel to address monitoring requirements, then policy coverage and responsiveness are improved, but system resource contention and performance degradation occur
Solution Approach 1:
The patent implements dynamic policy scheduling that adjusts policy execution timing based on real-time system workload conditions. The system continuously monitors system state and dynamically modifies policy schedules to avoid executing multiple policies simultaneously during peak workload periods, thereby preventing resource contention while maintaining comprehensive monitoring coverage
Solution Approach 2:
The system employs feedback mechanisms where policy execution outcomes and system performance metrics are continuously monitored and fed back to the policy scheduler. This feedback loop enables the system to learn from past executions and adjust future policy schedules to optimize both coverage and performance, avoiding scenarios that lead to resource contention
2Speed
If policy execution is scheduled to run immediately when conditions are met, then responsiveness to system events is improved, but resource starvation and execution delays occur due to peak workloads
Solution Approach 1:
The system performs preliminary analysis of system workload conditions before scheduling policy execution. By evaluating system state in advance and predicting potential resource contention, the scheduler can proactively adjust policy timing to avoid peak periods, ensuring policies execute promptly when resources are available while preventing execution delays during high-demand periods
Solution Approach 2:
The policy scheduling mechanism dynamically adapts execution timing based on real-time system conditions. When system resources are available, policies execute immediately to maintain responsiveness. When peak workload is detected, the system dynamically delays non-critical policy executions to avoid resource starvation, balancing speed and execution delay
3Device complexity
If static analysis and modifications of policy schedules are applied, then implementation simplicity is maintained, but inability to adapt to dynamic system conditions persists
Solution Approach 1:
The policy scheduling system implements self-service capabilities through automated workload analysis and adaptive schedule generation. The system autonomously monitors system conditions, evaluates policy execution patterns, and modifies schedules without requiring complex external configuration or manual intervention, thereby maintaining implementation simplicity while achieving dynamic adaptability
Solution Approach 2:
The system uses feedback from actual policy execution and system performance to continuously refine scheduling decisions. This automated feedback loop enables the system to adapt to dynamic conditions without requiring complex static pre-configurations, achieving versatility through learned patterns rather than intricate scheduling rules
Data Source
AI summary
A policy scheduler scheduling a policy is provided. The policy scheduler receives the policy for a system and information of a current state of the system. The policy scheduler evaluates one or more rules based on the current state of the system and generates a new rule via an evolutionary algorithm based on the information of the current state of the system. The policy scheduler adds the newly generated rule into the one or more rules and schedules the policy based on the one or more rules including the newly generated rule.