Clusterware Resource Dispersion Dependency Management
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Solution Overview
Problem
Clusterware systems face challenges in managing resource dependencies and ensuring high availability by efficiently dispersing resources across nodes to avoid co-location, which can lead to service disruptions and performance issues due to resource contention.
Innovation Solution
The implementation of dispersion dependencies in resource profiles, where attributes specify that resources should not be co-located, allowing the clusterware policy engine to manage resource placement based on dispersion metrics and active dispersion relations, ensuring that resources are distributed across nodes to maintain high availability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If resources are co-located on the same node to simplify management, then device complexity is reduced, but resource contention and service disruptions occur leading to reduced reliability
Solution Approach 1:
The patent segments resources into different types (compute, storage, network) and applies specific dispersion rules to each type. Resources are divided into critical and non-critical categories, with different placement strategies applied to each segment, allowing simplified management while maintaining reliability through targeted dispersion of critical resources across multiple nodes.
Solution Approach 2:
The patent implements local quality by applying different dispersion requirements to different resource types and locations. Critical resources receive stringent dispersion rules requiring placement on separate nodes, while non-critical resources allow more flexible co-location. This differentiated approach reduces overall management complexity while ensuring reliability for critical services.
2Reliability
If resources are dispersed across multiple nodes to prevent resource contention, then reliability is improved, but device complexity increases due to additional placement constraints
Solution Approach 1:
The patent creates a universal dispersion rule framework that applies to all resource types through a common policy engine. The same basic dispersion mechanisms and rule structures are used across compute, storage, and network resources, reducing placement complexity through standardized processes while maintaining high reliability through consistent application of dispersion principles across all critical resources.
Solution Approach 2:
The patent uses parameter changes by introducing dispersion weights and priority levels that can be adjusted to balance reliability requirements against placement complexity. By modifying these parameters, the system can dynamically adjust the strictness of dispersion rules based on current cluster conditions, service criticality, and available capacity, optimizing the trade-off between reliability and management complexity.
3Reliability
If strict dispersion rules are enforced to prevent co-location, then service availability is improved, but productivity decreases due to reduced resource utilization efficiency
Solution Approach 1:
The patent applies partial dispersion rules selectively to critical resources rather than enforcing strict dispersion on all resources. By applying dispersion requirements only where necessary for high availability (critical services) and allowing co-location for non-critical resources, the system maintains high availability for important services while improving overall resource utilization efficiency and productivity through better packing density.
Solution Approach 2:
The patent implements dynamic dispersion rules that can be adjusted based on current cluster conditions, workload characteristics, and service priorities. The system can dynamically modify dispersion requirements, relocate resources when conditions change, and balance high availability requirements against resource utilization efficiency in real-time, optimizing both reliability and productivity based on current operational needs.
4Productivity
If resources are freely placed on any available node to maximize resource utilization, then productivity is improved, but service disruptions occur due to resource contention leading to reduced reliability
Solution Approach 1:
The patent applies preliminary action by pre-establishing dispersion rules and constraints before resource placement occurs. The policy engine evaluates dispersion requirements and makes placement decisions in advance, preventing resource contention and service disruptions before they occur. This proactive approach ensures high resource utilization efficiency while maintaining service continuity by avoiding problematic co-locations from the outset.
Solution Approach 2:
The patent implements feedback mechanisms where the policy engine continuously monitors resource placement, utilization patterns, and service performance. Based on this feedback, the system can adjust dispersion rules, relocate resources to optimize both utilization efficiency and service continuity, and prevent resource contention by learning from past placement outcomes and performance data.
Data Source
AI summary
A method and apparatus for resource dispersion in a clusterware system is provided. The resource profiles for resources within a cluster of nodes are stored. Each resource profile includes attributes for a resource. One or more of the attributes in one of the resource profiles includes a dispersion dependency attribute that specifies that the resource is preferably not co-located with another resource. Resources are placed within the cluster based, at least in part, on the dispersion preference.