Compute Node Self-Priority Discovery for Security Clusters
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Solution Overview
Problem
Distributed physical security systems with heterogeneous compute nodes face challenges in efficiently managing resource allocation and redundancy due to varying hardware and software capabilities, leading to complexity and increased costs in configuration and maintenance.
Innovation Solution
A capability discovery system that determines self-priority values based on node capabilities, including license type, hardware, and current load, allowing for dynamic role assignment and service prioritization across nodes, enabling efficient resource utilization and simplified management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heterogeneous compute nodes are used in distributed security systems, then resource utilization and system capabilities are improved, but configuration and maintenance complexity increases
Solution Approach 1:
Each compute node automatically determines its own self-priority value based on its capabilities and current load, and publishes this information to other nodes. This self-service mechanism eliminates the need for centralized configuration management, allowing heterogeneous nodes to autonomously participate in service provisioning without increasing operational complexity.
Solution Approach 2:
The system transforms complex heterogeneous node capabilities into a single scalar parameter (self-priority value) that captures the essence of node suitability for service provision. This parameter aggregation simplifies the selection process while preserving the benefits of hardware and software capability differences across nodes.
2Reliability
If multiple compute nodes are deployed, then system reliability and redundancy are improved, but resource allocation complexity increases
Solution Approach 1:
Compute nodes continuously monitor their own load conditions and adjust their self-priority values accordingly. This feedback mechanism ensures that service requests are dynamically routed to nodes with appropriate capacity, maintaining system reliability while simplifying resource allocation through automatic load-aware scheduling.
Solution Approach 2:
The system creates an equipotential environment where all compute nodes, regardless of heterogeneity, can participate in service provisioning by publishing their self-priority values. This equalizes the playing field for node selection, allowing simple priority-based scheduling to effectively utilize diverse node capabilities without complex allocation logic.
3Ease of operation
If homogeneous compute nodes are used, then configuration and maintenance are simplified, but resource utilization efficiency decreases
Solution Approach 1:
Each node autonomously determines its self-priority based on its specific capabilities and current state, enabling heterogeneous nodes to self-manage their participation in service provisioning. This self-service approach maintains operational simplicity while fully utilizing the diverse resources available across different node types.
Solution Approach 2:
The system allows each compute node to have its own local characteristics (hardware capabilities, software capabilities, license types) reflected in its self-priority value. This local quality approach enables optimal utilization of each node's specific strengths while maintaining simple centralized service selection logic.
Data Source
AI summary
A physical security system is described comprising a simplified method for selection of a compute node from a cluster of compute nodes with which to assign a role or acquire a service. The method determines a scalar priority value for compute nodes in the cluster, and allows selection of a compute node by simply choosing the highest priority scalar value. Scalar priority values may be determined by one or more of: a compute node license type, capacity limits, a hardware capability, a software capability, and a current node load.