Context-Aware Dynamic Policy Selection for Load Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional load balancing techniques rely on statically defined policies, which are inflexible and unable to adapt to changing network conditions, making them inadequate for large-scale and multi-tenancy platforms like data centers and cloud computing service networks.
Innovation Solution
A context-aware dynamic policy selection method that uses OAMP data to dynamically update or create new load balancing policies, allowing load balancers to adjust their behavior in response to changing conditions by selecting policies from a pool stored in a rule repository, utilizing frameworks like the DEN-ng model for information model processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If statically defined policies are used for load balancing, then device complexity is reduced and ease of operation is improved, but adaptability to changing network conditions deteriorates
Solution Approach 1:
The patent implements dynamic policy selection by enabling load balancers to switch between multiple policies based on real-time network conditions. The system maintains a pool of policies with different characteristics (conservative, moderate, aggressive) and dynamically selects appropriate policies according to current traffic patterns and network state, transforming the static policy approach into a dynamic adaptive system.
Solution Approach 2:
The system changes policy parameters dynamically by adjusting which policy from the pool is active based on monitored network conditions. The policy manager evaluates metrics such as traffic load, response times, and error rates to determine optimal policy parameters, allowing the load balancing behavior to adapt to changing network environments without manual reconfiguration.
2Productivity
If dynamically updated policies are implemented, then adaptability to changing network conditions is improved, but device complexity and measurement precision requirements increase
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring network performance metrics and using this information to dynamically adjust policy selection. The policy manager collects OAMP data from load balancers and network elements, analyzes performance outcomes, and uses this feedback to select more effective policies, creating a closed-loop control system that improves resource utilization over time.
Solution Approach 2:
The patent introduces a policy manager as an intermediary component that bridges the gap between raw OAMP data and policy selection decisions. This intermediary layer processes and interprets complex network data, translating it into actionable policy choices, thereby reducing the difficulty of measuring and analyzing network conditions while enabling dynamic policy adaptation.
3Adaptability or versatility
If multiple policies are maintained in a rule repository, then versatility and adaptability are improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent segments the policy management system by organizing policies into a structured pool with distinct categories and characteristics. Each policy in the repository is a discrete, independently manageable unit with specific properties (conservative, moderate, aggressive). This segmentation allows the system to maintain multiple policies without creating monolithic complexity, enabling selective activation based on network conditions.
Solution Approach 2:
The policy pool serves multiple functions simultaneously: it provides a repository for stored policies, a selection mechanism for dynamic adaptation, and a framework for policy comparison and evaluation. The same policy pool structure supports different policy types and can be extended to accommodate new policies without fundamental system changes, enhancing versatility while managing complexity through universal design.
Data Source
AI summary
Dynamically updating load balancing policies based on operations, administration, maintenance, and provisioning (OAMP) data generated by a load balancing network may provide increased load balancing performance. As an example, an existing set of load-balancing policies can be dynamically modified based on OAMP data generated by load balancers and/or network elements. As another example, new load-balancing policies can be dynamically created based on the OAMP data. As yet another example, an updated set of load-balancing policies can be selected from a pool of policies based on OAMP data. Dynamically updating load balancing policies can be achieved using information model processing frameworks, such as the next generation directory enabled networks (DEN-ng) model.